<?xml version="1.0" encoding="UTF-8"?>
 <rdf:RDF xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://web.resource.org/cc/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/" xmlns:admin="http://webns.net/mvcb/">
  <channel rdf:about="http://pinboard.in">
    <title>Pinboard (Vaguery)</title>
    <link>https://pinboard.in/u:Vaguery/public/</link>
    <description>recent bookmarks from Vaguery</description>
    <items>
      <rdf:Seq>	<rdf:li rdf:resource="https://bookstore.ams.org/view?ProductCode=MCL/25"/>
	<rdf:li rdf:resource="https://bookstore.ams.org/view?ProductCode=MCL/26"/>
	<rdf:li rdf:resource="https://norvig.com/lispy.html"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2010.13178"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1902.10486"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1707.07201"/>
	<rdf:li rdf:resource="http://lights.climagic.org/"/>
	<rdf:li rdf:resource="https://research.birmingham.ac.uk/portal/files/54992747/Bartels_Wagenaar_Doubt_and_excitement_Qualitative_Research_2018.pdf"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1811.06889"/>
	<rdf:li rdf:resource="https://svpow.com/2019/01/23/birds-have-balance-organs-in-their-butts-why-is-no-one-talking-about-this/"/>
	<rdf:li rdf:resource="https://amiealbrecht.com/2018/08/05/quarterthecross-card-sort/"/>
	<rdf:li rdf:resource="https://www.degruyter.com/view/j/pjbr.2013.4.issue-1/pjbr-2013-0003/pjbr-2013-0003.xml"/>
	<rdf:li rdf:resource="https://design.google/library/ux-ai/"/>
	<rdf:li rdf:resource="https://meaningness.com/metablog/upgrade-your-cargo-cult"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1705.00960"/>
	<rdf:li rdf:resource="https://www.maa.org/external_archive/devlin/LockhartsLament.pdf"/>
	<rdf:li rdf:resource="http://codingdojo.org/WhatIsCodingDojo/"/>
	<rdf:li rdf:resource="http://designobserver.com/feature/empathy-in-book-publishing/39603"/>
	<rdf:li rdf:resource="https://ntguardian.wordpress.com/2017/05/29/winning-the-battle-for-riddler-nation-an-agent-based-modelling-approach/"/>
	<rdf:li rdf:resource="https://www.nytimes.com/2017/06/10/opinion/sunday/cuny-ending-the-curse-of-remedial-math.html"/>
	<rdf:li rdf:resource="https://hapgood.us/2016/05/13/choral-explanations/"/>
	<rdf:li rdf:resource="http://blog.mrmeyer.com/2017/you-cant-break-math/"/>
	<rdf:li rdf:resource="https://meteuphoric.wordpress.com/2017/01/04/why-read-old-philosophy/"/>
	<rdf:li rdf:resource="https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b#.8h6djsm3a"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1612.00347"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1506.06204"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1506.04782"/>
	<rdf:li rdf:resource="http://karpathy.github.io/2015/05/21/rnn-effectiveness/"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1410.5447"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1412.4878"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1412.1398"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1501.01891"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1312.0531"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1311.7434"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1411.1398"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1406.7444"/>
	<rdf:li rdf:resource="http://www.theguardian.com/books/2014/apr/20/frederic-gros-walk-nietzsche-kant"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1310.3167"/>
	<rdf:li rdf:resource="http://researchutopia.wordpress.com/2013/11/10/understanding-p-values-via-simulations/"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1304.1247"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1310.8428"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1309.5401"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1309.4283"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1302.3757"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1308.6546"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1302.2489"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1303.4385"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1304.1903"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1304.1819"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1303.7032"/>
	<rdf:li rdf:resource="http://rosalind.info/problems/locations/"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1303.2130"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1301.3630"/>
	<rdf:li rdf:resource="http://www.economist.com/blogs/babbage/2012/10/photography?fsrc=gn_ep&amp;test=babbage"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1112.6209"/>
	<rdf:li rdf:resource="http://www.thevalve.org/go/valve/article/weve_got_the_time_to_rationalize_the_text/"/>
	<rdf:li rdf:resource="http://financialagile.com/reflections/7-finance/89-another-sacred-cow-to-be-killed"/>
	<rdf:li rdf:resource="http://eplex.cs.ucf.edu/noveltysearch/userspage/index.html"/>
	<rdf:li rdf:resource="http://johnaugust.com/2011/self-taught-and-self-doubting"/>
	<rdf:li rdf:resource="http://www.r-bloggers.com/friday-fun-projects/?utm_source=feedburner&amp;utm_medium=feed&amp;utm_campaign=Feed%3A+RBloggers+%28R+bloggers%29"/>
	<rdf:li rdf:resource="http://apenwarr.ca/log/?m=201103"/>
	<rdf:li rdf:resource="http://www2.stetson.edu/~efriedma/puzzle.html"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1006.2404"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1006.0849"/>
	<rdf:li rdf:resource="http://en.wikipedia.org/wiki/Multi-task_learning"/>
	<rdf:li rdf:resource="http://projecteuler.net/index.php?section=about"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1005.0972"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/0904.4458"/>
	<rdf:li rdf:resource="http://codingdojo.org/cgi-bin/wiki.pl?KataFizzBuzz"/>
	<rdf:li rdf:resource="http://www.stat.columbia.edu/~cook/movabletype/archives/2010/03/building_a_bett.html"/>
      </rdf:Seq>
    </items>
  </channel><item rdf:about="https://bookstore.ams.org/view?ProductCode=MCL/25">
    <title>Mathematics via Problems: Part 1: Algebra</title>
    <dc:date>2024-09-20T15:41:41+00:00</dc:date>
    <link>https://bookstore.ams.org/view?ProductCode=MCL/25</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This book is a translation from Russian of Part I of the book Mathematics via Problems: From Olympiads and Math Circles to Profession. Part II, Geometry, and Part III, Combinatorics, have been published in the same series.

The main goal of this book is to develop important parts of mathematics through problems. The author tries to put together sequences of problems that allow high school students (and some undergraduates) with strong interest in mathematics to discover and recreate much of elementary mathematics and start edging into the sophisticated world of topics such as group theory, Galois theory, and so on, thus building a bridge (by showing that there is no gap) between standard high school exercises and more intricate and abstract concepts in mathematics.

Definitions and/or references for material that is not standard in the school curriculum are included. However, many topics in the book are difficult when you start learning them from scratch. To help with this, problems are carefully arranged to provide gradual introduction into each subject. Problems are often accompanied by hints and/or complete solutions.

The book is based on classes taught by the author at different times at the Independent University of Moscow, at a number of Moscow schools and math circles, and at various summer schools. It can be used by high school students and undergraduates, their teachers, and organizers of summer camps and math circles.

In the interest of fostering a greater awareness and appreciation of mathematics and its connections to other disciplines and everyday life, MSRI and the AMS are publishing books in the Mathematical Circles Library series as a service to young people, their parents and teachers, and the mathematics profession.

Titles in this series are co-published with the Mathematical Sciences Research Institute (MSRI).

]]></description>
<dc:subject>book mathematical-recreations education learning-by-doing to-read want</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:937cc80595f3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:book"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematical-recreations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:want"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://bookstore.ams.org/view?ProductCode=MCL/26">
    <title>Mathematics via Problems: Part 2: Geometry</title>
    <dc:date>2024-09-20T15:41:18+00:00</dc:date>
    <link>https://bookstore.ams.org/view?ProductCode=MCL/26</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This book is a translation from Russian of Part II of the book Mathematics via Problems: From Olympiads and Math Circles to Profession. Part I, Algebra, and Part III, Combinatorics, have been published in the same series.

The main goal of this book is to develop important parts of mathematics through problems. The authors tried to put together sequences of problems that allow high school students (and some undergraduates) with strong interest in mathematics to discover and recreate much of elementary mathematics and start edging into more sophisticated topics such as projective and affine geometry, solid geometry, and so on, thus building a bridge between standard high school exercises and more intricate notions in geometry.

Definitions and/or references for material that is not standard in the school curriculum are included. To help students that might be unfamiliar with new material, problems are carefully arranged to provide gradual introduction into each subject. Problems are often accompanied by hints and/or complete solutions.

The book is based on classes taught by the authors at different times at the Independent University of Moscow, at a number of Moscow schools and math circles, and at various summer schools. It can be used by high school students and undergraduates, their teachers, and organizers of summer camps and math circles.

In the interest of fostering a greater awareness and appreciation of mathematics and its connections to other disciplines and everyday life, MSRI and the AMS are publishing books in the Mathematical Circles Library series as a service to young people, their parents and teachers, and the mathematics profession.

Titles in this series are co-published with the Mathematical Sciences Research Institute (MSRI).

]]></description>
<dc:subject>book education learning-by-doing mathematical-recreations to-read want</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:75ad51377a0e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:book"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematical-recreations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:want"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://norvig.com/lispy.html">
    <title>(How to Write a (Lisp) Interpreter (in Python))</title>
    <dc:date>2024-06-24T12:06:14+00:00</dc:date>
    <link>https://norvig.com/lispy.html</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>learning-by-doing parsing computer-science basics</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9568e60dc05f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:parsing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computer-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:basics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2010.13178">
    <title>[2010.13178] Geometric Exploration for Online Control</title>
    <dc:date>2020-12-07T21:10:25+00:00</dc:date>
    <link>https://arxiv.org/abs/2010.13178</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We study the control of an \emph{unknown} linear dynamical system under general convex costs. The objective is minimizing regret vs. the class of disturbance-feedback-controllers, which encompasses all stabilizing linear-dynamical-controllers. In this work, we first consider the case of known cost functions, for which we design the first polynomial-time algorithm with n3T‾‾√-regret, where n is the dimension of the state plus the dimension of control input. The T‾‾√-horizon dependence is optimal, and improves upon the previous best known bound of T2/3. The main component of our algorithm is a novel geometric exploration strategy: we adaptively construct a sequence of barycentric spanners in the policy space. Second, we consider the case of bandit feedback, for which we give the first polynomial-time algorithm with poly(n)T‾‾√-regret, building on Stochastic Bandit Convex Optimization.
]]></description>
<dc:subject>online-learning machine-learning algorithms mathematical-programming learning-by-doing to-write-about consider:simulation consider:vector-version-in-SR</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0ed2737d2678/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:online-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematical-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:vector-version-in-SR"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1902.10486">
    <title>[1902.10486] On Tiny Episodic Memories in Continual Learning</title>
    <dc:date>2020-07-22T11:22:35+00:00</dc:date>
    <link>https://arxiv.org/abs/1902.10486</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In continual learning (CL), an agent learns from a stream of tasks leveraging prior experience to transfer knowledge to future tasks. It is an ideal framework to decrease the amount of supervision in the existing learning algorithms. But for a successful knowledge transfer, the learner needs to remember how to perform previous tasks. One way to endow the learner the ability to perform tasks seen in the past is to store a small memory, dubbed episodic memory, that stores few examples from previous tasks and then to replay these examples when training for future tasks. In this work, we empirically analyze the effectiveness of a very small episodic memory in a CL setup where each training example is only seen once. Surprisingly, across four rather different supervised learning benchmarks adapted to CL, a very simple baseline, that jointly trains on both examples from the current task as well as examples stored in the episodic memory, significantly outperforms specifically designed CL approaches with and without episodic memory. Interestingly, we find that repetitive training on even tiny memories of past tasks does not harm generalization, on the contrary, it improves it, with gains between 7\% and 17\% when the memory is populated with a single example per class.
]]></description>
<dc:subject>machine-learning architecture dynamics learning-by-doing episodic-memory to-write-about to-simulate consider:ReQ consider:online-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7403b9d78c77/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:architecture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:episodic-memory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:ReQ"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:online-learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1707.07201">
    <title>[1707.07201] PRIMES STEP Plays Games</title>
    <dc:date>2020-04-19T12:36:53+00:00</dc:date>
    <link>https://arxiv.org/abs/1707.07201</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[A group of students in 7-9 grades are inventing combinatorial impartial games. The games are played on graphs, piles, and grids. We found winning positions, optimal strategies, and other interesting facts about the games.
]]></description>
<dc:subject>mathematical-recreations pedagogy education learning-by-doing to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3bd20eb20ceb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematical-recreations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pedagogy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lights.climagic.org/">
    <title>climagic - LED Lights that you can control from the command line</title>
    <dc:date>2019-06-04T15:58:55+00:00</dc:date>
    <link>http://lights.climagic.org/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[While everyone jokes about turning off someone else's lights over the Internet, you can actually do it. Welcome to lights.climagic.com, where you and perhaps hundreds of others can use their command line skills to control these lights in interesting ways. You can also control the lights using the number keys on your keyboard on this webpage or by touching/clicking the lights, but that's boring and slow. A programming interface offers you a level of control that your hands can't match and the command line makes it quick and easy to write a complex program.
The 9 LED lights (numbered 1 to 9 from left to right) shown in the live video stream are sitting on a shelf in my house and are controlled by sending a UDP packet with a number to lights.climagic.com on port 45444. Sending a number will flip the state of the LED between on and off. There is only one set of lights and anyone can change the state of a light at any moment so your results may be unpredictable if others are also controlling the lights at the same time. It helps to shrink your browser window a bit and bring up a small terminal next to it. Have fun!
]]></description>
<dc:subject>demo social-psychology learning-by-doing rather-interesting internet-of-intention to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:fbbe7dc2f03e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:demo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:internet-of-intention"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://research.birmingham.ac.uk/portal/files/54992747/Bartels_Wagenaar_Doubt_and_excitement_Qualitative_Research_2018.pdf">
    <title>[PDF] Doubt and Excitement: An Experiential Learning Approach to Teaching the Practice of Qualitative Research</title>
    <dc:date>2019-04-25T15:32:22+00:00</dc:date>
    <link>https://research.birmingham.ac.uk/portal/files/54992747/Bartels_Wagenaar_Doubt_and_excitement_Qualitative_Research_2018.pdf</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This article diagnoses that qualitative research (QR) methods courses and literature often remain silent on how to actually do QR and explores how practice theory can improve learning and teaching the practice of QR. It develops an experiential learning approach of turning experiences and emotions of doubt and excitement into a dialogical process of asking creative questions, imagining new ideas, and animating a practical relationship to the world. Based on data and observations of a summer school course in QR methods to PhD students, we present three pedagogical practices for recognizing and tolerating affective resistances to experiential learning and finding c]]></description>
<dc:subject>pedagogy programming learning-by-doing the-mangle-of-practice research how-to-think to-read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:eebea15e419e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pedagogy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-of-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:how-to-think"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1811.06889">
    <title>[1811.06889] On the Complexity of Exploration in Goal-Driven Navigation</title>
    <dc:date>2019-04-23T10:47:00+00:00</dc:date>
    <link>https://arxiv.org/abs/1811.06889</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Building agents that can explore their environments intelligently is a challenging open problem. In this paper, we make a step towards understanding how a hierarchical design of the agent's policy can affect its exploration capabilities. First, we design EscapeRoom environments, where the agent must figure out how to navigate to the exit by accomplishing a number of intermediate tasks (\emph{subgoals}), such as finding keys or opening doors. Our environments are procedurally generated and vary in complexity, which can be controlled by the number of subgoals and relationships between them. Next, we propose to measure the complexity of each environment by constructing dependency graphs between the goals and analytically computing \emph{hitting times} of a random walk in the graph. We empirically evaluate Proximal Policy Optimization (PPO) with sparse and shaped rewards, a variation of policy sketches, and a hierarchical version of PPO (called HiPPO) akin to h-DQN. We show that analytically estimated \emph{hitting time} in goal dependency graphs is an informative metric of the environment complexity. We conjecture that the result should hold for environments other than navigation. Finally, we show that solving environments beyond certain level of complexity requires hierarchical approaches.
]]></description>
<dc:subject>exploration-and-exploitation agents machine-learning learning-by-doing engineering-design planning rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2c2610386ee8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:exploration-and-exploitation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agents"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://svpow.com/2019/01/23/birds-have-balance-organs-in-their-butts-why-is-no-one-talking-about-this/">
    <title>Birds have balance organs in their butts. Why is no-one talking about this?! | Sauropod Vertebra Picture of the Week</title>
    <dc:date>2019-02-07T11:03:02+00:00</dc:date>
    <link>https://svpow.com/2019/01/23/birds-have-balance-organs-in-their-butts-why-is-no-one-talking-about-this/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[I was equally blown away by the fact that I’d never heard about this from inside the world of science and sci-comm. Necker’s discovery seemed to have been almost entirely overlooked in the broader comparative anatomy community. I searched for weaknesses in the evidence or reasoning, and I also searched for people debunking the idea that birds have balance organs in their butts, and in both cases I came up empty-handed (if you know of counter-evidence, please let me know!). It’s relevant to paleontology, too: because the lumbosacral canals occupy transverse recesses in the roof of the sacral neural canal, they should be discoverable in fossil taxa. I’ve never heard of them being identified in a non-avian dinosaur, but then, I’ve never heard of anyone looking. You can also see the lumbosacral canals for yourself, or at least the spaces they occupy, for about three bucks, as I will show in an upcoming post.

Incidentally, I’m pretty sure this system underlies the axiomatic ability of birds to run around with their heads cut off. I grew up on a farm and raised and slaughtered chickens, so I’ve observed this firsthand. A decapitated chicken can get up on its hind legs and run around. It won’t go very far or in a straight line, hence the jokey expression, but it can actually run on flat ground. It hadn’t occurred to me until recently how weird that is. All vertebrates have central pattern generators in their spinal cords that can produce the basic locomotor movements of the trunk and limbs, but if you decapitate most vertebrates the body will just lie there and twitch. The limbs may even make rudimentary running motions, but the decapitated body can’t stand up and successfully walk or run. Central pattern generators aren’t enough, to run you need an organ of balance. A decapitated bird can successfully stand and run around because it still has a balance organ, in its lumbosacral spinal cord.

]]></description>
<dc:subject>physiology biology anatomy rather-interesting learning-by-doing life-finds-a-way</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2ba7b656810f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:physiology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:anatomy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:life-finds-a-way"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://amiealbrecht.com/2018/08/05/quarterthecross-card-sort/">
    <title>#QuarterTheCross Card Sort – Wonder in Mathematics</title>
    <dc:date>2018-08-09T12:03:58+00:00</dc:date>
    <link>https://amiealbrecht.com/2018/08/05/quarterthecross-card-sort/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[It is no secret that Quarter the Cross is one of my favourite tasks. I’ve written about it twice before: as a Day 1 activity and in connection with Fraction Talks. The original source is apparently T. Dekker & N. Querelle, 2002, Great Assessment Problems (www.fi.uu.nl/catch). It has proliferated in recent years, including with an active Twitter hashtag: #QuarterTheCross.

]]></description>
<dc:subject>mathematical-recreations nudge-targets consider:novelty-search innovation to-write-about learning-by-doing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:cddb13e84947/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematical-recreations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:novelty-search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:innovation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.degruyter.com/view/j/pjbr.2013.4.issue-1/pjbr-2013-0003/pjbr-2013-0003.xml">
    <title>Robot Skill Learning: From Reinforcement Learning to Evolution Strategies : Paladyn, Journal of Behavioral Robotics</title>
    <dc:date>2018-04-02T11:38:41+00:00</dc:date>
    <link>https://www.degruyter.com/view/j/pjbr.2013.4.issue-1/pjbr-2013-0003/pjbr-2013-0003.xml</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Policy improvement methods seek to optimize the parameters of a policy with respect to a utility function. Owing to current trends involving searching in parameter space (rather than action space) and using reward-weighted averaging (rather than gradient estimation), reinforcement learning algorithms for policy improvement, e.g. PoWER and PI2, are now able to learn sophisticated high-dimensional robot skills. A side-effect of these trends has been that, over the last 15 years, reinforcement learning (RL) algorithms have become more and more similar to evolution strategies such as (μW , λ)-ES and CMA-ES. Evolution strategies treat policy improvement as a black-box optimization problem, and thus do not leverage the problem structure, whereas RL algorithms do. In this paper, we demonstrate how two straightforward simplifications to the state-of-the-art RL algorithm PI2 suffice to convert it into the black-box optimization algorithm (μW, λ)-ES. Furthermore, we show that (μW , λ)-ES empirically outperforms PI2 on the tasks in [36]. It is striking that PI2 and (μW , λ)-ES share a common core, and that the simpler algorithm converges faster and leads to similar or lower final costs. We argue that this difference is due to a third trend in robot skill learning: the predominant use of dynamic movement primitives (DMPs). We show how DMPs dramatically simplify the learning problem, and discuss the implications of this for past and future work on policy improvement for robot skill learning

]]></description>
<dc:subject>robotics machine-learning metaheuristics algorithms learning-by-doing engineering-design planning to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d44d476a4be3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaheuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://design.google/library/ux-ai/">
    <title>The UX of AI - Library - Google Design</title>
    <dc:date>2018-01-27T23:27:01+00:00</dc:date>
    <link>https://design.google/library/ux-ai/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[As was the case with the mobile revolution, and the web before that, machine learning will cause us to rethink, restructure, and reconsider what’s possible in virtually every experience we build. In the Google UX community, we’ve started an effort called “human-centered machine learning” to help focus and guide that conversation. Using this lens, we look across products to see how machine learning (ML) can stay grounded in human needs while solving for them—in ways that are uniquely possible through ML. Our team at Google works across the company to bring UXers up to speed on core ML concepts, understand how to best integrate ML into the UX utility belt, and ensure we're building ML and AI in inclusive ways.
]]></description>
<dc:subject>image-processing the-mangle-in-practice philosophy-of-engineering user-experience learning-by-doing to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:cc8d84470a9d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:user-experience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://meaningness.com/metablog/upgrade-your-cargo-cult">
    <title>Upgrade your cargo cult for the win | Meaningness</title>
    <dc:date>2017-12-23T10:21:41+00:00</dc:date>
    <link>https://meaningness.com/metablog/upgrade-your-cargo-cult</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The problem with the cargo cults is not that they are imitating. It’s that their members are not legitimate participants in airport operation.
Imagine a cargo cult downloaded all the manuals for ground crew procedures from the web, and watched thousands of hours of videos of competent ground crews doing their jobs. Imagine they learned them perfectly, and were able to execute them perfectly.
Still no airline would be willing to use their airport. The cult is not certified for operation; it is not legitimate. The proper bureaucratic rituals have not been observed. These rituals are rational: there has to be a fixed procedure for assuring that a ground crew is competent, and making special exceptions could be disastrous. “These cultists sure seem to know what they are doing; let’s create a set of tests to verify that, without putting them through our usual training regimen”? That would risk airplanes and lives, and would probably end the careers of everyone involved.
]]></description>
<dc:subject>credentialing academic-culture learning-by-doing communities-of-practice essay have-read have-done very-nice advice</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:942e9255f15b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:credentialing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:academic-culture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:communities-of-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:essay"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:have-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:have-done"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:very-nice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:advice"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1705.00960">
    <title>[1705.00960] Foundations of Intelligent Additive Manufacturing</title>
    <dc:date>2017-09-29T14:23:34+00:00</dc:date>
    <link>https://arxiv.org/abs/1705.00960</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[During the last decade, additive manufacturing has become increasingly popular for rapid prototyping, but has remained relatively marginal beyond the scope of prototyping when it comes to applications with tight tolerance specifications, such as in aerospace. Despite a strong desire to supplant many aerospace structures with printed builds, additive manufacturing has largely remained limited to prototyping, tooling, fixtures, and non-critical components. There are numerous fundamental challenges inherent to additive processing to be addressed before this promise is realized. One ubiquitous challenge across all AM motifs is to develop processing-property relationships through precise, in situ monitoring coupled with formal methods and feedback control. We suggest a significant component of this vision is a set of semantic layers within 3D printing files relevant to the desired material specifications. This semantic layer provides the feedback laws of the control system, which then evaluates the component during processing and intelligently evolves the build parameters within boundaries defined by semantic specifications. This evaluation and correction loop requires on-the-fly coupling of finite element analysis and topology optimization. The required parameters for this analysis are all extracted from the semantic layer and can be modified in situ to satisfy the global specifications. Therefore, the representation of what is printed changes during the printing process to compensate for eventual imprecision or drift arising during the manufacturing process.
]]></description>
<dc:subject>additivism 3d-printing engineering-design philosophy-of-engineering learning-by-doing to-write-about consider:linking-to-manifesto</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a7e472c96637/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:additivism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:3d-printing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:linking-to-manifesto"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.maa.org/external_archive/devlin/LockhartsLament.pdf">
    <title>Lockhart's Lament [PDF]</title>
    <dc:date>2017-09-23T11:16:56+00:00</dc:date>
    <link>https://www.maa.org/external_archive/devlin/LockhartsLament.pdf</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>mathematical-recreations education learning-by-doing pedagogy cultural-engineering public-policy learning-in-public</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9c6d0b4b0c17/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematical-recreations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pedagogy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cultural-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:public-policy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-in-public"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://codingdojo.org/WhatIsCodingDojo/">
    <title>WhatIsCodingDojo - Coding Dojo</title>
    <dc:date>2017-09-19T12:05:48+00:00</dc:date>
    <link>http://codingdojo.org/WhatIsCodingDojo/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[A Coding Dojo is a meeting where a bunch of coders get together to work on a programming challenge. They are there have fun and to engage in DeliberatePractice in order to improve their skills.

The ParisDojo focuses on coding in front of others, most often something from scratch, in a very short amount of time (1 to 1.5 hours). They use various languages, various tools, various exercise formats. They consider the outcome of an exercise successful when it is completed within allocated time AND audience can repeat the exercise at home by themselves.

Maybe the CodingDojoPrinciples help to understand what the CodingDojo is about.]]></description>
<dc:subject>coding-dojo software-development-is-not-programming learning-by-doing mindfulness exercises to-write-about the-mangle-in-practice</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4e91b73fc02c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:coding-dojo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:software-development-is-not-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mindfulness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:exercises"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://designobserver.com/feature/empathy-in-book-publishing/39603">
    <title>Empathy in Book Publishing: Design Observer</title>
    <dc:date>2017-07-22T12:44:59+00:00</dc:date>
    <link>http://designobserver.com/feature/empathy-in-book-publishing/39603</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Unsurprisingly, the best book designers tend to be avid readers, which could be framed as an introspective form of customer empathy. But human-centered design is about immersing in your customer’s experience—your reader’s experience. That’s different than your experience of reading. Only being an avid reader falls short of customer immersion. A book designer’s work primarily focuses on the author, editor, and book—the reader typically receives tangential attention. What would happen if book designers went further and immersed in their readers’ lives? I decided to find out.

One of my imprints publishes books by thought leaders in literacy instruction. Our readers are teachers who teach students how to love reading books (yes, very meta). So, I began volunteering in the same 4th grade classroom for one full day each month. I didn't conduct interviews, collect data, or keep a journal. I simply made an effort to be present with a mind towards spotting the teacher’s "peas." Tiny things, like how the gnawing sound of an electric pencil sharpener distracts students. And how the ubiquitous spiral binding on a grading book turns the ruler that tracks a student’s row across the spread into an irritating seesaw. Immersing in the teacher’s experience also brought me face-to-face with my customer’s customer—children. During one independent reading session, I noticed a girl who had cast her book aside with a frown. I asked why. She said it lacked drama. I began dramatically presenting alternate titles. She frowned harder. The period ended. I was struck by a very plain fact: you can’t force someone to read. 

My classroom experiences didn't uncover any 80 million dollar ideas, although if I ever have the opportunity to design a teacher’s grading book, I will lobby for lay-flat binding—or, better yet, an app. Looking at the world through my customer’s eyes did, however, change the way I view my job. Back in the office discussions about “the customer” were no longer abstract. I now felt a responsibility to advocate for the teachers and students I had met. The act of immersing in my customer’s experience suddenly felt as fundamental to my charge as the act of kerning. 

You don't need the letters U and X in your job title to adopt a customer-needs perspective. All designers, no matter their level, should count fostering customer empathy in themselves and others as a baseline job requirement—doubly so if you work in book publishing where human-centered design is seldom discussed. Invest energy into spotting your readers’ peas. Internalize their perspective. Champion their needs. I can’t promise it will make you rich, but it will imbue your work with a greater sense of service and purpose.]]></description>
<dc:subject>books user-experience publishing learning-by-doing empathy the-mangle-is-other-people</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1d84c5a19b02/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:user-experience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:publishing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:empathy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-is-other-people"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ntguardian.wordpress.com/2017/05/29/winning-the-battle-for-riddler-nation-an-agent-based-modelling-approach/">
    <title>Winning the Battle for Riddler Nation; An Agent-Based Modelling Approach to the Solution | Curtis Miller's Personal Website</title>
    <dc:date>2017-06-16T10:29:53+00:00</dc:date>
    <link>https://ntguardian.wordpress.com/2017/05/29/winning-the-battle-for-riddler-nation-an-agent-based-modelling-approach/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Oliver Roeder runs a column on FiveThirtyEight called “The Riddler,” where he proposes logical and mathematical puzzles for readers to solve. On February 3rd of this year, he posted in Riddler Classic the problem, “Can You Rule Riddler Nation?” Here is the description:

]]></description>
<dc:subject>mathematical-recreations puzzles learning-by-doing to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:fc1c140c17f8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematical-recreations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:puzzles"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nytimes.com/2017/06/10/opinion/sunday/cuny-ending-the-curse-of-remedial-math.html">
    <title>Ending the Curse of Remedial Math - The New York Times</title>
    <dc:date>2017-06-11T11:33:41+00:00</dc:date>
    <link>https://www.nytimes.com/2017/06/10/opinion/sunday/cuny-ending-the-curse-of-remedial-math.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The CUNY Start model is different. Full-time students are exclusively in Start classes for 25 hours a week — substantially more than the usual course load — for one semester. The focus is on thinking, not memorization. “Math isn’t just memorization,” Ms. Fells told me. “I teach them how to investigate problems — how to think. The first sentence on the first day is a question. We start by making a connection to real life and slowly build a foundation of knowledge for more abstract algebraic problems. I never say you are right or wrong. The answers come from them.”

]]></description>
<dc:subject>learning-by-doing pedagogy public-policy to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:51c6d889007e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pedagogy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:public-policy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://hapgood.us/2016/05/13/choral-explanations/">
    <title>Choral Explanations | Hapgood</title>
    <dc:date>2017-03-31T11:44:45+00:00</dc:date>
    <link>https://hapgood.us/2016/05/13/choral-explanations/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Unlike wiki, however, individual control of writing is preserved, and multiple unique passes at a subject are appreciated. And big questions get a lot of passes. Here’s a snapshot of a few of the sixty-eight responses to Quora’s question of why many physicists believe in a multiverse.

]]></description>
<dc:subject>collective-behavior wisdom-of-crowds social-media learning-by-doing education</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d72c53afa79c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-behavior"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:education"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://blog.mrmeyer.com/2017/you-cant-break-math/">
    <title>You Can’t Break Math – dy/dan</title>
    <dc:date>2017-02-19T23:05:40+00:00</dc:date>
    <link>http://blog.mrmeyer.com/2017/you-cant-break-math/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[BTW

I haven’t found a way to generate these kinds of insights about math without surrounding myself with people learning math for the first time.
One of my most enduring shortcomings as a teacher is how much I plan and revise those plans, even if the lesson I have on file will suffice. I’ll chase a scintilla of an improvement for hours, which was never sustainable. I spent most of the previous day prepping this Desmos activity. We used 10% of it.
Language from the day that I’m still pondering: “We cancel the 2x’s because we want to get x by itself.” I’m trying to decide if those italicized expressions contribute to a student’s understanding of large ideas about mathematics or of small ideas about solving a particular kind of equation.
]]></description>
<dc:subject>pedagogy the-mangle-in-practice learning-by-doing looking-to-see rather-interesting mathematics to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6ca67a01f4ea/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pedagogy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://meteuphoric.wordpress.com/2017/01/04/why-read-old-philosophy/">
    <title>Why read old philosophy? | Meteuphoric</title>
    <dc:date>2017-01-10T13:20:25+00:00</dc:date>
    <link>https://meteuphoric.wordpress.com/2017/01/04/why-read-old-philosophy/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Here’s my explanation. Reading Aristotle describe his thoughts about the world is like watching Aristotle ride a skateboard if Aristotle were a pro skater. You are not getting value from learning about the streets he is gliding over (or the natural world that he is describing) and you should not be memorizing the set of jumps he chooses (or his particular conceptualizations of the world). You are meant to be learning about how to carry out the activity that he is carrying out: how to be Aristotle. How to do what Aristotle would do, even in a new environment.

An old work of philosophy does not describe the thing you are meant to be learning about. It was created by the thing you are meant to be learning about, much like watching a video from skater-Aristotle’s GoPro. And the value proposition is that with this high resolution Aristotle’s-eye-view, you can infer the motions.

There is not a short description  of the insights you should learn (or at least not one available), because the insights you are hopefully learning are not the insights that Aristotle is trying to share. Aristotle might have highly summarizable insights, but what you want to know is how to be Aristotle, and nobody has necessarily developed an abstract model of how to be Aristotle from which summary statements can be extracted.

]]></description>
<dc:subject>philosophy philosophy-of-science very-good essay learning-by-doing learning-by-watching the-mangle-in-practice to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:056947a3acae/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:very-good"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:essay"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-watching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b#.8h6djsm3a">
    <title>Yes you should understand backprop – Medium</title>
    <dc:date>2016-12-23T12:46:50+00:00</dc:date>
    <link>https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b#.8h6djsm3a</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[When we offered CS231n (Deep Learning class) at Stanford, we intentionally designed the programming assignments to include explicit calculations involved in backpropagation on the lowest level. The students had to implement the forward and the backward pass of each layer in raw numpy. Inevitably, some students complained on the class message boards]]></description>
<dc:subject>neural-networks numerical-methods learning-by-doing algorithms pedagogy doing-it-wrong-on-purpose</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ef0f7322732c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:numerical-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pedagogy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:doing-it-wrong-on-purpose"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1612.00347">
    <title>[1612.00347] Bootstrapping incremental dialogue systems: using linguistic knowledge to learn from minimal data</title>
    <dc:date>2016-12-17T18:43:40+00:00</dc:date>
    <link>https://arxiv.org/abs/1612.00347</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We present a method for inducing new dialogue systems from very small amounts of unannotated dialogue data, showing how word-level exploration using Reinforcement Learning (RL), combined with an incremental and semantic grammar - Dynamic Syntax (DS) - allows systems to discover, generate, and understand many new dialogue variants. The method avoids the use of expensive and time-consuming dialogue act annotations, and supports more natural (incremental) dialogues than turn-based systems. Here, language generation and dialogue management are treated as a joint decision/optimisation problem, and the MDP model for RL is constructed automatically. With an implemented system, we show that this method enables a wide range of dialogue variations to be automatically captured, even when the system is trained from only a single dialogue. The variants include question-answer pairs, over- and under-answering, self- and other-corrections, clarification interaction, split-utterances, and ellipsis. This generalisation property results from the structural knowledge and constraints present within the DS grammar, and highlights some limitations of recent systems built using machine learning techniques only.
]]></description>
<dc:subject>dialog learning-by-doing learning-by-watching reinforcement-learning algorithms rather-interesting artificial-intelligence natural-language-processing nudge-targets consider:representation consider:architecture</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f57bf5ec8799/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dialog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-watching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reinforcement-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:natural-language-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:architecture"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1506.06204">
    <title>[1506.06204] Learning to Segment Object Candidates</title>
    <dc:date>2015-08-09T12:51:39+00:00</dc:date>
    <link>http://arxiv.org/abs/1506.06204</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Recent object detection systems rely on two critical steps: (1)~a set of object proposals is predicted as efficiently as possible, and (2)~this set of candidate proposals is then passed to an object classifier. Such approaches have been shown they can be fast, while achieving the state of the art in detection performance. In this paper, we propose a new way to generate object proposals, introducing an approach based on a discriminative convolutional network. Our model is trained jointly with two objectives: given an image patch, the first part of the system outputs a class-agnostic segmentation mask, while the second part of the system outputs the likelihood of the patch being centered on a full object. At test time, the model is efficiently applied on the whole test image and generates a set of segmentation masks, each of them being assigned with a corresponding object likelihood score. We show that our model yields significant improvements over state-of-the-art object proposal algorithms. In particular, compared to previous approaches, our model obtains substantially higher object recall using fewer proposals. We also show that our model is able to generalize to unseen categories it has not seen during training. Unlike all previous approaches for generating object masks, we do not rely on edges, superpixels, or any other form of low-level segmentation.
]]></description>
<dc:subject>image-processing image-segmentation machine-learning algorithms learning-by-doing nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:44c68473516f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-segmentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1506.04782">
    <title>[1506.04782] Cheap Bandits</title>
    <dc:date>2015-07-25T15:15:01+00:00</dc:date>
    <link>http://arxiv.org/abs/1506.04782</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider stochastic sequential learning problems where the learner can observe the \textit{average reward of several actions}. Such a setting is interesting in many applications involving monitoring and surveillance, where the set of the actions to observe represent some (geographical) area. The importance of this setting is that in these applications, it is actually \textit{cheaper} to observe average reward of a group of actions rather than the reward of a single action. We show that when the reward is \textit{smooth} over a given graph representing the neighboring actions, we can maximize the cumulative reward of learning while \textit{minimizing the sensing cost}. In this paper we propose CheapUCB, an algorithm that matches the regret guarantees of the known algorithms for this setting and at the same time guarantees a linear cost again over them. As a by-product of our analysis, we establish a Ω(dT‾‾‾√) lower bound on the cumulative regret of spectral bandits for a class of graphs with effective dimension d.
]]></description>
<dc:subject>bandit-problems planning mechanism-design agent-based learning-by-doing learning-by-watching nudge-targets consider:rediscovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6a54adf1ca6f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bandit-problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mechanism-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-watching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:rediscovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://karpathy.github.io/2015/05/21/rnn-effectiveness/">
    <title>The Unreasonable Effectiveness of Recurrent Neural Networks</title>
    <dc:date>2015-07-10T22:13:56+00:00</dc:date>
    <link>http://karpathy.github.io/2015/05/21/rnn-effectiveness/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[There's something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning. Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense. Sometimes the ratio of how simple your model is to the quality of the results you get out of it blows past your expectations, and this was one of those times. What made this result so shocking at the time was that the common wisdom was that RNNs were supposed to be difficult to train (with more experience I've in fact reached the opposite conclusion). Fast forward about a year: I'm training RNNs all the time and I've witnessed their power and robustness many times, and yet their magical outputs still find ways of amusing me. This post is about sharing some of that magic with you.

]]></description>
<dc:subject>neural-networks via:many machine-learning learning-by-doing tutorial explanation generative-art recurrent-neural-nets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a6a9393012c7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:many"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tutorial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:explanation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:recurrent-neural-nets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1410.5447">
    <title>[1410.5447] Recognizing molecular patterns by machine learning: an agnostic structural definition of the hydrogen bond</title>
    <dc:date>2015-07-05T12:01:39+00:00</dc:date>
    <link>http://arxiv.org/abs/1410.5447</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The concept of chemical bonding can ultimately be seen as a rationalization of the recurring structural patterns observed in molecules and solids. Chemical intuition is nothing but the ability to recognize and predict such patterns, and how they transform into one another. Here we discuss how to use a computer to identify atomic patterns automatically, so as to provide an algorithmic definition of a bond based solely on structural information. We concentrate in particular on hydrogen bonding -- a central concept to our understanding of the physical chemistry of water, biological systems and many technologically important materials. Since the hydrogen bond is a somewhat fuzzy entity that covers a broad range of energies and distances, many different criteria have been proposed and used over the years, based either on sophisticate electronic structure calculations followed by an energy decomposition analysis, or on somewhat arbitrary choices of a range of structural parameters that is deemed to correspond to a hydrogen-bonded configuration. We introduce here a definition that is univocal, unbiased, and adaptive, based on our machine-learning analysis of an atomistic simulation. The strategy we propose could be easily adapted to similar scenarios, where one has to recognize or classify structural patterns in a material or chemical compound.
]]></description>
<dc:subject>chemistry machine-learning looking-to-see learning-by-doing learning-by-watching rather-interesting nudge-targets consider:feature-discovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1823ecd1c627/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:chemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-watching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:feature-discovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1412.4878">
    <title>[1412.4878] Functional Automata - Formal Languages for Computer Science Students</title>
    <dc:date>2015-07-01T11:02:38+00:00</dc:date>
    <link>http://arxiv.org/abs/1412.4878</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[An introductory formal languages course exposes advanced undergraduate and early graduate students to automata theory, grammars, constructive proofs, computability, and decidability. Programming students find these topics to be challenging or, in many cases, overwhelming and on the fringe of Computer Science. The existence of this perception is not completely absurd since students are asked to design and prove correct machines and grammars without being able to experiment nor get immediate feedback, which is essential in a learning context. This article puts forth the thesis that the theory of computation ought to be taught using tools for actually building computations. It describes the implementation and the classroom use of a library, FSM, designed to provide students with the opportunity to experiment and test their designs using state machines, grammars, and regular expressions. Students are able to perform random testing before proceeding with a formal proof of correctness. That is, students can test their designs much like they do in a programming course. In addition, the library easily allows students to implement the algorithms they develop as part of the constructive proofs they write. Providing students with this ability ought to be a new trend in the formal languages classroom.
]]></description>
<dc:subject>automata computer-science finite-state-machines pedagogy learning-by-doing rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0ae32df686b1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computer-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:finite-state-machines"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pedagogy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1412.1398">
    <title>[1412.1398] Space Exploration via Proximity Search</title>
    <dc:date>2015-03-15T21:50:54+00:00</dc:date>
    <link>http://arxiv.org/abs/1412.1398</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We investigate what computational tasks can be performed on a point set in ℜd, if we are only given black-box access to it via nearest-neighbor search. This is a reasonable assumption if the underlying point set is either provided implicitly, or it is stored in a data structure that can answer such queries. In particular, we show the following: (A) One can compute an approximate bi-criteria k-center clustering of the point set, and more generally compute a greedy permutation of the point set. (B) One can decide if a query point is (approximately) inside the convex-hull of the point set. 
We also investigate the problem of clustering the given point set, such that meaningful proximity queries can be carried out on the centers of the clusters, instead of the whole point set.
]]></description>
<dc:subject>computational-complexity computational-geometry algorithms rather-interesting planning learning-by-doing nudge-targets statistics sampling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:344954ceef27/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computational-complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computational-geometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sampling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1501.01891">
    <title>[1501.01891] From art to geometry: aesthetic and beauty in the learning process</title>
    <dc:date>2015-02-01T22:43:02+00:00</dc:date>
    <link>http://arxiv.org/abs/1501.01891</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Starting from the concept that knowledge comes as element of mediation between the convergent thinking, founded on experience, and the divergent thinking, placed in the perceptive, intuitive, creative dimension, in this paper we want to present an idea for developing an educational path combining the concept of beauty and some historical notes. It is possible to use this dissertation as a starting point to conceive a geometric laboratory that drawing inspiration from artistic works, get to create geometric shapes provided with fascinating symmetries
]]></description>
<dc:subject>symmetry aesthetics generative-art rather-interesting psychology learning-by-doing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:75a36b47c2cf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symmetry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aesthetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1312.0531">
    <title>[1312.0531] Optimal A Priori Balance in the Design of Controlled Experiments</title>
    <dc:date>2014-12-24T12:15:51+00:00</dc:date>
    <link>http://arxiv.org/abs/1312.0531</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We develop a unified theory of designs for controlled experiments that balance baseline covariates a priori (before treatment and before randomization) using the framework of minimax variance. We establish a "no free lunch" theorem that indicates that, without structural information on the dependence of potential outcomes on baseline covariates, complete randomization is optimal. Restricting the structure of dependence, either parametrically or non-parametrically, leads directly to imbalance metrics and optimal designs. Certain choices of this structure recover known imbalance metrics and designs previously developed ad hoc, including randomized block designs, pairwise-matched designs, and re-randomization. New choices of structure based on reproducing kernel Hilbert spaces lead to new methods, both parametric and non-parametric.
]]></description>
<dc:subject>experimental-design no-free-lunch statistics planning rather-interesting philosophy-of-engineering modeling learning-by-doing nudge-targets consider:data-design consider:stress-testing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:829d6390b76d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experimental-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:no-free-lunch"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:data-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:stress-testing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1311.7434">
    <title>[1311.7434] Observability, Identifiability and Sensitivity of Vision-Aided Navigation</title>
    <dc:date>2014-11-16T11:40:07+00:00</dc:date>
    <link>http://arxiv.org/abs/1311.7434</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We analyze the observability of motion estimates from the fusion of visual and inertial sensors. Because the model contains unknown parameters, such as sensor biases, the problem is usually cast as a mixed identification/filtering, and the resulting observability analysis provides a necessary condition for any algorithm to converge to a unique point estimate. Unfortunately, most models treat sensor bias rates as noise, independent of other states including biases themselves, an assumption that is patently violated in practice. When this assumption is lifted, the resulting model is not observable, and therefore past analyses cannot be used to conclude that the set of states that are indistinguishable from the measurements is a singleton. In other words, the resulting model is not observable. We therefore re-cast the analysis as one of sensitivity: Rather than attempting to prove that the indistinguishable set is a singleton, which is not the case, we derive bounds on its volume, as a function of characteristics of the input and its sufficient excitation. This provides an explicit characterization of the indistinguishable set that can be used for analysis and validation purposes.
]]></description>
<dc:subject>robotics algorithms formalization the-mangle-in-practice sensor-integration learning-by-doing how-very-odd nudge-targets performance-measure who-exactly-is-observability-for?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4736fb2501ac/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:formalization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sensor-integration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:how-very-odd"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:who-exactly-is-observability-for?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1411.1398">
    <title>[1411.1398] Reservoir computing with a single time-delay autonomous Boolean node</title>
    <dc:date>2014-11-13T22:11:43+00:00</dc:date>
    <link>http://arxiv.org/abs/1411.1398</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We demonstrate reservoir computing with a physical system using a single autonomous Boolean logic element with time-delay feedback. The system generates a chaotic transient with a window of consistency lasting between 30 and 300 ns, which we show is sufficient for reservoir computing. We then characterize the dependence of computational performance on system parameters to find the best operating point of the reservoir. When the best parameters are chosen, the reservoir is able to classify short input patterns with performance that decreases over time. In particular, we show that four distinct input patterns can be classified for 70 ns, even though the inputs are only provided to the reservoir for 7.5 ns.
]]></description>
<dc:subject>computer-science alternative-computer-architectures experiment memory nudge-targets rather-interesting learning-by-doing machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:8ebb4ca6fc8c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computer-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:alternative-computer-architectures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:memory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1406.7444">
    <title>[1406.7444] Learning to Deblur</title>
    <dc:date>2014-09-28T10:51:09+00:00</dc:date>
    <link>http://arxiv.org/abs/1406.7444</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We describe a learning-based approach to blind image deconvolution. It uses a deep layered architecture, parts of which are borrowed from recent work on neural network learning, and parts of which incorporate computations that are specific to image deconvolution. The system is trained end-to-end on a set of artificially generated training examples, enabling competitive performance in blind deconvolution, both with respect to quality and runtime.
]]></description>
<dc:subject>image-processing deep-learning neural-networks algorithms machine-learning nudge-targets contextual-modeling performance-measure learning-by-doing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:163c452ba1c2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:contextual-modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.theguardian.com/books/2014/apr/20/frederic-gros-walk-nietzsche-kant">
    <title>Frédéric Gros: why going for a walk is the best way to free your mind | Books | The Observer</title>
    <dc:date>2014-04-27T11:57:29+00:00</dc:date>
    <link>http://www.theguardian.com/books/2014/apr/20/frederic-gros-walk-nietzsche-kant</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Walking is of no profit, it is only benefit, he says. Though the best quote of his is about when considering any course of action, one should ask: could someone do it in my place? And if the answer is yes, give it up.

"Yes. You can be replaced at your work, but not for your walk. Living, in the deepest sense, is something that no one else can do for us."

Walking, says Gros, is "exploring the mystery of presence. Presence to the world, to others and to yourself... You discover when you walk that it emancipates you from space and time, from… vitesse."

]]></description>
<dc:subject>philosophy learning-by-doing perambulation thinking styles</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ecdf3e11f0fc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:perambulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:thinking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:styles"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1310.3167">
    <title>[1310.3167] Well-Posedness And Accuracy Of The Ensemble Kalman Filter In Discrete And Continuous Time</title>
    <dc:date>2014-03-31T11:16:25+00:00</dc:date>
    <link>http://arxiv.org/abs/1310.3167</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The ensemble Kalman filter (EnKF) is a method for combining a dynamical model with data in a sequential fashion. Despite its widespread use, there has been little analysis of its theoretical properties. Many of the algorithmic innovations associated with the filter, which are required to make a useable algorithm in practice, are derived in an ad hoc fashion. The aim of this paper is to initiate the development of a systematic analysis of the EnKF, in particular to do so in the small ensemble size limit. The perspective is to view the method as a state estimator, and not as an algorithm which approximates the true filtering distribution. The perturbed observation version of the algorithm is studied, without and with variance inflation. Without variance inflation well-posedness of the filter is established; with variance inflation accuracy of the filter, with resepct to the true signal underlying the data, is established. The algorithm is considered in discrete time, and also for a continuous time limit arising when observations are frequent and subject to large noise. The underlying dynamical model, and assumptions about it, is sufficiently general to include the Lorenz '63 and '96 models, together with the incompressible Navier-Stokes equation on a two-dimensional torus. The analysis is limited to the case of complete observation of the signal with additive white noise. Numerical results are presented for the Navier-Stokes equation on a two-dimensional torus for both complete and partial observations of the signal with additive white noise.
]]></description>
<dc:subject>theory-and-practice-sitting-in-a-tree statistics algorithms interesting learning-from-data learning-by-doing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b33bdbc4df9b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theory-and-practice-sitting-in-a-tree"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-from-data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://researchutopia.wordpress.com/2013/11/10/understanding-p-values-via-simulations/">
    <title>Understanding p-values via simulations | Research Utopia</title>
    <dc:date>2014-02-07T15:17:50+00:00</dc:date>
    <link>http://researchutopia.wordpress.com/2013/11/10/understanding-p-values-via-simulations/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this first simulation, we will examine the behaviour of the p-value when the difference between two groups is null. I simulated 100,000 experiments, with 100 participants in each group. The mean of each group was fixed at 100, and the standard deviation was fixed at 20. For each participant, the computer set as their score a randomly selected number from a normal distribution with a mean of 100 and SD of 20. At the end of each experiment, the computer performs a t-test, and records the observed p-value. At the end, a histogram is plotted which shows the frequency of each p-value across the entire simulation.

]]></description>
<dc:subject>via:mark.larios statistics scientific-computing learning-by-doing folk-statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:83b80b78c073/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:mark.larios"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:scientific-computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:folk-statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1304.1247">
    <title>[1304.1247] Solving Linear Programming with Constraints Unknown</title>
    <dc:date>2013-12-01T13:10:19+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.1247</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[What is the value of input information in solving linear programming? The celebrated ellipsoid algorithm tells us that the full information of input constraints is not necessary; the algorithm works as long as there exists an oracle that, on a proposed candidate solution, returns a violation in the format of a separating hyperplane. Can linear programming still be efficiently solved if the returned violation is in other formats? 
We study this question in a trial-and-error framework: there is an oracle that, upon a proposed solution, returns the index of a violated constraint (with the content of the constraint still hidden). When more than one constraint is violated, two variants in the model are investigated. (1) The oracle returns the index of a "most violated" constraint, measured by the Euclidean distance of the proposed solution and the half-spaces defined by the constraints. In this case, the LP can be efficiently solved. (2) The oracle returns the index of an arbitrary (i.e., worst-case) violated constraint. In this case, we give an algorithm with running time exponential in the number of variables. We then show that the exponential dependence on n is unfortunately necessary even for the query complexity. These results put together shed light on the amount of information that one needs in order to solve a linear program efficiently. 
The proofs of the results employ a variety of geometric techniques, including McMullen's Upper Bound Theorem, the weighted spherical Voronoi diagram, and the furthest Voronoi diagram. In addition, we give an alternative proof to a conjecture of L\'aszl\'o Fejes T\'oth on bounding the number of disconnected components formed by the union of m convex bodies in R^n. Our proof, inspired by the Gauss-Bonnet Theorem in global differential geometry, is independent of the known and reveals more clear insights into the problem and the bound.
]]></description>
<dc:subject>linear-programming algorithms learning-by-doing trial-and-error nudge-targets constraint-satisfaction</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a1fe986d1700/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:linear-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:trial-and-error"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:constraint-satisfaction"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1310.8428">
    <title>[1310.8428] Multilabel Classification throughout Random Graph Ensembles</title>
    <dc:date>2013-11-03T12:25:05+00:00</dc:date>
    <link>http://arxiv.org/abs/1310.8428</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We present new methods for multilabel classification, relying on ensemble learning on a collection of random output graphs imposed on the multilabel and a kernel-based structured output learner as the base classifier. For ensemble learning, differences among the output graphs provide the required base classifier diversity and lead to improved performance in the increasing size of the ensemble. We study different methods of forming the ensemble prediction, including majority voting and two methods that perform inferences over the graph structures before or after combining the base models into the ensemble. We compare the methods against the state-of-the-art machine learning approaches on a set of heterogeneous multilabel benchmark problems, including multilabel AdaBoost, convex multitask feature learning, as well as single target learning approaches represented by Bagging and SVM. In our experiments, the random graph ensembles are very competitive and robust, ranking first or second on most of the datasets. Overall, our results show that random graph ensembles are viable alternatives to flat multilabel and multitask learners.
]]></description>
<dc:subject>classification algorithms learning-by-doing nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a8861e5aaad1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1309.5401">
    <title>[1309.5401] Nonmyopic View Planning for Active Object Detection</title>
    <dc:date>2013-09-24T11:13:02+00:00</dc:date>
    <link>http://arxiv.org/abs/1309.5401</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[One of the central problems in computer vision is the detection of semantically important objects and the estimation of their pose. Most of the work in object detection has been based on single image processing and its performance is limited by occlusions and ambiguity in appearance and geometry. This paper proposes an active approach to object detection by controlling the point of view of a mobile depth camera. When an initial static detection phase identifies an object of interest, several hypotheses are made about its class and orientation. The sensor then plans a sequence of views, which balances the amount of energy used to move with the chance of identifying the correct hypothesis. We formulate an active hypothesis testing problem, which includes sensor mobility, and solve it using a point-based approximate POMDP algorithm. The validity of our approach is verified through simulation and real-world experiments with the PR2 robot. The results suggest that our approach outperforms the widely-used greedy view point selection and provides a significant improvement over static object detection.
]]></description>
<dc:subject>image-segmentation image-analysis learning-by-doing agent-based nudge-targets intentional-learning-methods</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:187d04718d1d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-segmentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:intentional-learning-methods"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1309.4283">
    <title>[1309.4283] Neuromorphic Learning towards Nano Second Precision</title>
    <dc:date>2013-09-22T19:51:00+00:00</dc:date>
    <link>http://arxiv.org/abs/1309.4283</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Temporal coding is one approach to representing information in spiking neural networks. An example of its application is the location of sounds by barn owls that requires especially precise temporal coding. Dependent upon the azimuthal angle, the arrival times of sound signals are shifted between both ears. In order to deter- mine these interaural time differences, the phase difference of the signals is measured. We implemented this biologically inspired network on a neuromorphic hardware system and demonstrate spike-timing dependent plasticity on an analog, highly accelerated hardware substrate. Our neuromorphic implementation enables the resolution of time differences of less than 50 ns. On-chip Hebbian learning mechanisms select inputs from a pool of neurons which code for the same sound frequency. Hence, noise caused by different synaptic delays across these inputs is reduced. Furthermore, learning compensates for variations on neuronal and synaptic parameters caused by device mismatch intrinsic to the neuromorphic substrate.
]]></description>
<dc:subject>neural-networks biologically-inspired learning-by-doing signal-processing nudge-targets algorithms experiment</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6604534c400d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biologically-inspired"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:signal-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1302.3757">
    <title>[1302.3757] How to predict community responses to perturbations in the face of imperfect knowledge and network complexity</title>
    <dc:date>2013-09-20T12:26:32+00:00</dc:date>
    <link>http://arxiv.org/abs/1302.3757</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[It is a challenge to predict the response of a large, complex system to a perturbation. Recent attempts to predict the behaviour of food webs have revealed that the effort needed to understand a system grows quickly with its complexity, because increasingly precise information on the elements of the system is required. Here, we show that not all elements of the system need to be measured equally well. This suggests that a more efficient allocation of effort to understand a complex systems is possible. We develop an iterative technique for determining an efficient measurement strategy. Finally, in our assessment of model food webs, we find that it is most important to precisely measure the mortality and predation rates of long-lived, generalist, top predators. Prioritizing the study of such species will make it easier to understand the response of complex food webs to perturbations.
]]></description>
<dc:subject>food-webs ecology inference learning-by-doing statistics prediction nudge-targets consider:agent-based</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9c3194d88b44/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:food-webs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:ecology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:agent-based"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1308.6546">
    <title>[1308.6546] Self tolerance in a minimal model of the idiotypic network</title>
    <dc:date>2013-08-31T20:21:54+00:00</dc:date>
    <link>http://arxiv.org/abs/1308.6546</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider the problem of self tolerance in the frame of a minimalistic model of the idiotypic network. A node of this network represents a population of B lymphocytes of the same idiotype which is encoded by a bit string. The links of the network connect nodes with (nearly) complementary strings. The population of a node survives if the number of occupied neighbours is not too small and not too large. There is an influx of lymphocytes with random idiotype from the bone marrow. Previous investigations have shown that this system evolves toward highly organized architectures, where the nodes can be classified into groups according to their statistical properties. The building principles of these architectures can be analytically described and the statistical results of simulations agree very well with results of a modular mean field theory. In this paper we present simulation results for the case that one or several nodes, playing the role of self, are permanently occupied. We observe that the group structure of the architecture is very similar to the case without self antigen, but organized such that the neighbours of the self are only weakly occupied, thus providing self tolerance. We also treat this situation in mean field theory which give results in good agreement with data from simulation.
]]></description>
<dc:subject>artificial-life immunology theoretical-biology learning-by-doing complexology nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:53037547ac58/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:immunology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1302.2489">
    <title>[1302.2489] Adaptive-treed bandits</title>
    <dc:date>2013-08-26T22:13:09+00:00</dc:date>
    <link>http://arxiv.org/abs/1302.2489</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We describe a novel algorithm for continuum-armed bandits and noisy global optimisation, with good convergence properties over any continuous reward function having finitely many polynomial maxima. Over such functions, our algorithm achieves square-root cumulative regret in bandits, and inverse-square-root error in optimisation, without prior information. 
Our algorithm works by reducing these problems to tree-armed bandits, and we also provide new results in this setting. We show it is possible to adaptively combine multiple trees so as to minimise the regret, and also give near-matching lower bounds on the regret in terms of the zooming dimension.
]]></description>
<dc:subject>algorithms optimal-experimental-design sampling learning-by-doing nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a32df4d2379a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:optimal-experimental-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sampling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1303.4385">
    <title>[1303.4385] Modeling a Sensor to Improve its Efficacy</title>
    <dc:date>2013-04-12T13:20:47+00:00</dc:date>
    <link>http://arxiv.org/abs/1303.4385</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Robots rely on sensors to provide them with information about their surroundings. However, high-quality sensors can be extremely expensive and cost-prohibitive. Thus many robotic systems must make due with lower-quality sensors. Here we demonstrate via a case study how modeling a sensor can improve its efficacy when employed within a Bayesian inferential framework. As a test bed we employ a robotic arm that is designed to autonomously take its own measurements using an inexpensive LEGO light sensor to estimate the position and radius of a white circle on a black field. The light sensor integrates the light arriving from a spatially distributed region within its field of view weighted by its Spatial Sensitivity Function (SSF). We demonstrate that by incorporating an accurate model of the light sensor SSF into the likelihood function of a Bayesian inference engine, an autonomous system can make improved inferences about its surroundings. The method presented here is data-based, fairly general, and made with plug-and play in mind so that it could be implemented in similar problems.]]></description>
<dc:subject>robotics sensors machine-learning embedded-systems learning-by-doing nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0cf935f18756/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sensors"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:embedded-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1304.1903">
    <title>[1304.1903] Towards a living earth simulator</title>
    <dc:date>2013-04-12T13:00:41+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.1903</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The Living Earth Simulator (LES) is one of the core components of the FuturICT architecture. It will work as a federation of methods, tools, techniques and facilities supporting all of the FuturICT simulation-related activities to allow and encourage interactive exploration and understanding of societal issues. Society-relevant problems will be targeted by leaning on approaches based on complex systems theories and data science in tight interaction with the other components of FuturICT. The LES will evaluate and provide answers to real-world questions by taking into account multiple scenarios. It will build on present approaches such as agent-based simulation and modeling, multiscale modelling, statistical inference, and data mining, moving beyond disciplinary borders to achieve a new perspective on complex social systems.]]></description>
<dc:subject>simulation ecology systems complexology data-analysis learning-by-doing nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:83251567bb65/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:ecology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1304.1819">
    <title>[1304.1819] Model-based Bayesian Reinforcement Learning for Dialogue Management</title>
    <dc:date>2013-04-12T11:14:03+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.1819</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Reinforcement learning methods are increasingly used to optimise dialogue policies from experience. Most current techniques are model-free: they directly estimate the utility of various actions, without explicit model of the interaction dynamics. In this paper, we investigate an alternative strategy grounded in model-based Bayesian reinforcement learning. Bayesian inference is used to maintain a posterior distribution over the model parameters, reflecting the model uncertainty. This parameter distribution is gradually refined as more data is collected and simultaneously used to plan the agent's actions. Within this learning framework, we carried out experiments with two alternative formalisations of the transition model, one encoded with standard multinomial distributions, and one structured with probabilistic rules. We demonstrate the potential of our approach with empirical results on a user simulator constructed from Wizard-of-Oz data in a human-robot interaction scenario. The results illustrate in particular the benefits of capturing prior domain knowledge with high-level rules.]]></description>
<dc:subject>reinforcement-learning artificial-intelligence learning-by-doing models nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9590fed87803/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reinforcement-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1303.7032">
    <title>[1303.7032] A Massively Parallel Associative Memory Based on Sparse Neural Networks</title>
    <dc:date>2013-04-08T20:15:54+00:00</dc:date>
    <link>http://arxiv.org/abs/1303.7032</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Associative memories store content in such a way that the content can be later retrieved by presenting the memory with a small portion of the content, rather than presenting the memory with an address as in more traditional memories. Associative memories are used as building blocks for algorithms within database engines, anomaly detection systems, compression algorithms, and face recognition systems. The classical example of an associative memory is the Hopfield neural network. Recently, Gripon and Berrou have introduced an alternative construction which builds on ideas from the theory of error correcting codes and which greatly outperforms the Hopfield network in capacity, diversity, and efficiency. In this paper we implement a variation of the Gripon-Berrou associative memory on a general purpose graphical processing unit (GPU). The work of Gripon and Berrou proposes two retrieval rules, sum-of-sum and sum-of-max. The sum-of-sum rule uses only matrix-vector multiplication and is easily implemented on the GPU. The sum-of-max rule is much less straightforward to implement because it involves non-linear operations. However, the sum-of-max rule gives significantly better retrieval error rates. We propose a hybrid rule tailored for implementation on a GPU which achieves a 760-fold speedup without sacrificing any accuracy.]]></description>
<dc:subject>neural-networks emergent-design associative-memory learning-by-doing algorithms nudge-targets nudge reminds-me-of-that-dude-at-SFI</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e0328dc88ecc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:emergent-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:associative-memory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reminds-me-of-that-dude-at-SFI"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://rosalind.info/problems/locations/">
    <title>ROSALIND | Problems | Locations</title>
    <dc:date>2013-04-03T11:27:04+00:00</dc:date>
    <link>http://rosalind.info/problems/locations/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Rosalind is a platform for learning bioinformatics through problem solving.]]></description>
<dc:subject>bioinformatics learning-by-doing programming pedagogy to-emulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9f6c0d69a189/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pedagogy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-emulate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1303.2130">
    <title>[1303.2130] Convex Discriminative Multitask Clustering</title>
    <dc:date>2013-03-31T11:47:17+00:00</dc:date>
    <link>http://arxiv.org/abs/1303.2130</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Multitask clustering tries to improve the clustering performance of multiple tasks simultaneously by taking their relationship into account. Most existing multitask clustering algorithms fall into the type of generative clustering, and none are formulated as convex optimization problems. In this paper, we propose two convex Discriminative Multitask Clustering (DMTC) algorithms to address the problems. Specifically, we first propose a Bayesian DMTC framework. Then, we propose two convex DMTC objectives within the framework. The first one, which can be seen as a technical combination of the convex multitask feature learning and the convex Multiclass Maximum Margin Clustering (M3C), aims to learn a shared feature representation. The second one, which can be seen as a combination of the convex multitask relationship learning and M3C, aims to learn the task relationship. The two objectives are solved in a uniform procedure by the efficient cutting-plane algorithm. Experimental results on a toy problem and two benchmark datasets demonstrate the effectiveness of the proposed algorithms.]]></description>
<dc:subject>reasonable-in-principle confused-in-practice meta-optimization machine-learning algorithms nudge-targets learning-by-watching learning-by-doing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0fe961d4f433/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reasonable-in-principle"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:confused-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-watching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1301.3630">
    <title>[1301.3630] Behavior Pattern Recognition using A New Representation Model</title>
    <dc:date>2013-03-25T11:22:54+00:00</dc:date>
    <link>http://arxiv.org/abs/1301.3630</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We study the use of inverse reinforcement learning (IRL) as a tool for the recognition of agents' behavior on the basis of observation of their sequential decision behavior interacting with the environment. We model the problem faced by the agents as a Markov decision process (MDP) and model the observed behavior of the agents in terms of forward planning for the MDP. We use IRL to learn reward functions and then use these reward functions as the basis for clustering or classification models. Experimental studies with GridWorld, a navigation problem, and the secretary problem, an optimal stopping problem, suggest reward vectors found from IRL can be a good basis for behavior pattern recognition problems. Empirical comparisons of our method with several existing IRL algorithms and with direct methods that use feature statistics observed in state-action space suggest it may be superior for recognition problems.]]></description>
<dc:subject>learning-by-doing algorithms agent-based nudge-targets not-clear-on-the-inversion-thing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:402779885bf5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:not-clear-on-the-inversion-thing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.economist.com/blogs/babbage/2012/10/photography?fsrc=gn_ep&amp;test=babbage">
    <title>Photography: Difference Engine: Digital disillusion | The Economist</title>
    <dc:date>2012-10-10T21:04:31+00:00</dc:date>
    <link>http://www.economist.com/blogs/babbage/2012/10/photography?fsrc=gn_ep&amp;test=babbage</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[On the one hand, he appreciates the way digital cameras let him experiment endlessly by taking numerous shots of a scene, each time with a different exposure setting, and then deleting or editing the less successful ones until an all-but perfect image remains. On the other hand, he enjoys the challenge and forethought involved in setting up a shot with an analogue camera. The discipline of having only a dozen shots on a roll of 120 film concentrates the mind no end. Making every image count heightens the sense of achievement.

]]></description>
<dc:subject>photography art learning-by-doing constraints-as-supports</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6b903b3af07a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:photography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:constraints-as-supports"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1112.6209">
    <title>[1112.6209] Building high-level features using large scale unsupervised learning</title>
    <dc:date>2012-01-01T19:57:26+00:00</dc:date>
    <link>http://arxiv.org/abs/1112.6209</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider the problem of building detectors for high-level concepts using only unsupervised feature learning. For example, we would like to understand if it is possible to learn a face detector using only unlabeled images downloaded from the internet. To answer this question, we trained a simple feature learning algorithm on a large dataset of images (10 million images, each image is 200x200). The simulation is performed on a cluster of 1000 machines with fast network hardware for one week. Extensive experimental results reveal surprising evidence that such high-level concepts can indeed be learned using only unlabeled data and a simple learning algorithm.]]></description>
<dc:subject>image-analysis image-segmentation unsupervised-learning learning-by-doing feature-extraction nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e587fb55ad76/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-segmentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:unsupervised-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:feature-extraction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.thevalve.org/go/valve/article/weve_got_the_time_to_rationalize_the_text/">
    <title>The Valve - A Literary Organ | We’ve Got the Time (to Rationalize the Text)</title>
    <dc:date>2011-08-03T14:30:37+00:00</dc:date>
    <link>http://www.thevalve.org/go/valve/article/weve_got_the_time_to_rationalize_the_text/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["It takes, say, thousands of person hours spread over a handful of scholars to create and ‘debug’ a single conceptual trope. When that’s done the trope can show up in casebooks and undergraduate texts. And from there, it goes into the knowledge-hungry minds of our students. And when one of them writes reviews for The New York Times, BINGO! a conceptual trope enters the self-styled paper of record. And, from there, the world."

"That’s how culture works."

]]></description>
<dc:subject>criticism literary-criticism skills learning-by-doing critical-engineer</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1770a94833fc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:criticism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:literary-criticism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:skills"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:critical-engineer"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://financialagile.com/reflections/7-finance/89-another-sacred-cow-to-be-killed">
    <title>Another Sacred Cow To Be Killed: The Agile Retro</title>
    <dc:date>2011-06-01T11:17:06+00:00</dc:date>
    <link>http://financialagile.com/reflections/7-finance/89-another-sacred-cow-to-be-killed</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["The story of Goat Island has parallels for us engineers.  Because we cannot predict results, we know that patience, hope and courage are functions of the design process.  Every so often, we have to remind ourselves of that.  We also know that patience is a function of a good retrospective.  Just as it took a certain amount of time for the snappers to grow large enough to take on the urchins, I think there is a certain – and measurable – amount of time for participants in a retrospective to open up and start moving beyond the superficial.  That amount of time is more than two hours."]]></description>
<dc:subject>learning-by-doing retrospectives agile-practices collaboration</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0c3da1b80cc4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:retrospectives"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agile-practices"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collaboration"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://eplex.cs.ucf.edu/noveltysearch/userspage/index.html">
    <title>Novelty Search Users Page</title>
    <dc:date>2011-05-26T13:48:01+00:00</dc:date>
    <link>http://eplex.cs.ucf.edu/noveltysearch/userspage/index.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["This page provides information on the use and implementation of novelty search, an evolutionary search method that takes the radical step of ignoring the objective of search and instead rewarding only behavioral novelty. This visual demonstration (requires modern browser, IE users may need to install a plugin) contrasts a search for novelty with a search for the objective."]]></description>
<dc:subject>evolutionary-algorithms diversity innovation learning-by-doing gptp-2011</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:cf0a75e508e0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diversity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:innovation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:gptp-2011"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://johnaugust.com/2011/self-taught-and-self-doubting">
    <title>Taking the plunge | johnaugust.com</title>
    <dc:date>2011-05-16T11:38:37+00:00</dc:date>
    <link>http://johnaugust.com/2011/self-taught-and-self-doubting</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["You’ll be told it’s because it makes communicating your vision easier, and that’s true.  But there are two more important reasons.  First, if you know how to be a sound man, you know how to make the sound man’s job easier. This has the potential to make you very popular with sound men (or editors, or cinematographers, etc), something you’ll need when your only currency is good will.  Second, when you begin producing your own work, this renaissance approach to filmmaking will allow you to start before anyone else signs on.  Knowing you can finish in a pinch, if you have to, will lend you a confident relentlessness that makes others want to get involved."]]></description>
<dc:subject>generalism learning-by-doing advice</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:362d8fd38e7b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generalism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:advice"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.r-bloggers.com/friday-fun-projects/?utm_source=feedburner&amp;utm_medium=feed&amp;utm_campaign=Feed%3A+RBloggers+%28R+bloggers%29">
    <title>Friday fun projects | (R news &amp; tutorials)</title>
    <dc:date>2011-05-14T14:01:31+00:00</dc:date>
    <link>http://www.r-bloggers.com/friday-fun-projects/?utm_source=feedburner&amp;utm_medium=feed&amp;utm_campaign=Feed%3A+RBloggers+%28R+bloggers%29</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[At some point, I’ll turn to my favourite web application combo: Sinatra + MongoDB + Highcharts, to visualize these data dynamically on a web page. For now though, we can get a quick idea and create even more Friday fun by learning how to use RApache to run and view R code in the browser.

]]></description>
<dc:subject>Ruby R-language visualization statistics programming learning-by-doing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:199000794cb3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Ruby"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:R-language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://apenwarr.ca/log/?m=201103">
    <title>apenwarr - Business is Programming</title>
    <dc:date>2011-05-09T11:00:07+00:00</dc:date>
    <link>http://apenwarr.ca/log/?m=201103</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Whether because they're Canadian or because they're engineers, or both, they are unusual among aid organizations because they focus on understanding what didn't work. For the last three years, they've published Failure Reports detailing their specific failures. The reports make an interesting read, not just for aid organizations, but for anyone trying to manage engineering teams."]]></description>
<dc:subject>learning-by-doing publishing engineering-design social-norms explain-your-mistakes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:26e482bf6334/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:publishing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-norms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:explain-your-mistakes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www2.stetson.edu/~efriedma/puzzle.html">
    <title>Erich's Puzzle Palace</title>
    <dc:date>2011-05-07T11:35:19+00:00</dc:date>
    <link>http://www2.stetson.edu/~efriedma/puzzle.html</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>nudge-targets mathematical-recreations learning-by-doing genetic-programming-target</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:767848781f83/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematical-recreations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming-target"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1006.2404">
    <title>[1006.2404] Multiple-length-scale elastic instability mimics parametric resonance of nonlinear oscillators</title>
    <dc:date>2010-06-25T13:58:51+00:00</dc:date>
    <link>http://arxiv.org/abs/1006.2404</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Spatially confined rigid membranes reorganize their morphology in response to the imposed constraints. A crumpled elastic sheet presents a complex pattern of random folds focusing the deformation energy while compressing a membrane resting on a soft foundation creates a regular pattern of sinusoidal wrinkles with a broad distribution of energy. … The physical model, exhibiting an analogy with parametric resonance in nonlinear oscillator, is a new theoretical toolkit to understand the morphology of various confined systems, such as coated materials or living tissues, e.g., wrinkled skin, internal structure of lungs, internal elastica of an artery, brain convolutions or formation of fingerprints. Moreover, it opens the way to new kind of microfabrication design of multiperiodic or chaotic (aperiodic) surface topography via self-organization."
]]></description>
<dc:subject>physics models nudge-targets learning-by-doing simulable</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b30e3e2ed3a7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulable"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1006.0849">
    <title>[1006.0849] Reconstruction of Causal Networks by Set Covering</title>
    <dc:date>2010-06-09T12:18:54+00:00</dc:date>
    <link>http://arxiv.org/abs/1006.0849</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["We present a method for the reconstruction of networks, based on the order of nodes visited by a stochastic branching process. Our algorithm reconstructs a network of minimal size that ensures consistency with the data. Crucially, we show that global consistency with the data can be achieved through purely local considerations, inferring the neighbourhood of each node in turn. The optimisation problem solved for each individual node can be reduced to a Set Covering Problem, which is known to be NP-hard but can be approximated well in practice. We then extend our approach to account for noisy data, based on the Minimum Description Length principle. We demonstrate our algorithms on synthetic data, generated by an SIR-like epidemiological model."
]]></description>
<dc:subject>network-theory modeling statistics learning-from-data learning-by-doing algorithms nudge-targets</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:79e27f8dba6c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:network-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-from-data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://en.wikipedia.org/wiki/Multi-task_learning">
    <title>Multi-task learning - Wikipedia, the free encyclopedia</title>
    <dc:date>2010-05-25T20:58:23+00:00</dc:date>
    <link>http://en.wikipedia.org/wiki/Multi-task_learning</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Multi-task learning is an approach to machine learning, that learns a problem together with other related problems at the same time, using a shared representation. This often leads to a better model for the main task, because it allows the learner to use the commonality among the tasks. Therefore, multi-task learning is a kind of inductive transfer."
]]></description>
<dc:subject>I-guess machine-learning learning-by-doing learning-by-watching nudge-targets</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d78cd3f9d8a4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:I-guess"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-watching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuler.net/index.php?section=about">
    <title>Project Euler</title>
    <dc:date>2010-05-09T13:48:56+00:00</dc:date>
    <link>http://projecteuler.net/index.php?section=about</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Project Euler is a series of challenging mathematical/computer programming problems that will require more than just mathematical insights to solve. Although mathematics will help you arrive at elegant and efficient methods, the use of a computer and programming skills will be required to solve most problems.

The motivation for starting Project Euler, and its continuation, is to provide a platform for the inquiring mind to delve into unfamiliar areas and learn new concepts in a fun and recreational context."
]]></description>
<dc:subject>mathematics pedagogy archive learning-by-doing exercises puzzles challenges nudge-targets</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f55f6b2ccf09/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pedagogy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:archive"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:exercises"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:puzzles"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:challenges"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1005.0972">
    <title>[1005.0972] Adaptive Tuning Algorithm for Performance tuning of Database Management System</title>
    <dc:date>2010-05-09T12:04:04+00:00</dc:date>
    <link>http://arxiv.org/abs/1005.0972</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Performance tuning of Database Management Systems(DBMS) is both complex and challenging as it involves identifying and altering several key performance tuning parameters. The quality of tuning and the extent of performance enhancement achieved greatly depends on the skill and experience of the Database Administrator (DBA). As neural networks have the ability to adapt to dynamically changing inputs and also their ability to learn makes them ideal candidates for employing them for tuning purpose. In this paper, a novel tuning algorithm based on neural network estimated tuning parameters is presented. The key performance indicators are proactively monitored….The tuner alters these tuning parameters using the estimated values using a rate change computing algorithm. The preliminary results show that the proposed method is effective in improving the query response time for a variety of workload types."
]]></description>
<dc:subject>dba databases system-administration database-administration design-automation learning-by-doing learning-from-data nudge-targets</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:49d42d3b910d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dba"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:databases"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-administration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:database-administration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:design-automation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-from-data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0904.4458">
    <title>[0904.4458] Learning Character Strings via Mastermind Queries, with a Case Study Involving mtDNA</title>
    <dc:date>2010-04-25T12:30:04+00:00</dc:date>
    <link>http://arxiv.org/abs/0904.4458</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["We study the degree to which a character string, $Q$, leaks details about itself any time it engages in comparison protocols with a strings provided by a querier, Bob, even if those protocols are cryptographically guaranteed to produce no additional information other than the scores that assess the degree to which $Q$ matches strings offered by Bob. We show that such scenarios allow Bob to play variants of the game of Mastermind with $Q$ so as to learn the complete identity of $Q$."
]]></description>
<dc:subject>mathematical-recreations bioinformatics algorithms preprint learning-by-doing</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e13ec84a0bd2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematical-recreations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:preprint"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://codingdojo.org/cgi-bin/wiki.pl?KataFizzBuzz">
    <title>Coding Dojo Wiki: KataFizzBuzz</title>
    <dc:date>2010-03-19T21:10:24+00:00</dc:date>
    <link>http://codingdojo.org/cgi-bin/wiki.pl?KataFizzBuzz</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Imagine the scene. You are eleven years old, and in the five minutes before the end of the lesson, your Maths teacher decides he should make his class more "fun" by introducing a "game". He explains that he is going to point at each pupil in turn and ask them to say the next number in sequence, starting from one. The "fun" part is that if the number is divisible by three, you instead say "Fizz" and if it is divisible by five you say "Buzz". So now your maths teacher is pointing at all of your classmates in turn, and they happily shout "one!", "two!", "Fizz!", "four!", "Buzz!"... until he very deliberately points at you, fixing you with a steely gaze... time stands still, your mouth dries up, your palms become sweatier and sweatier until you finally manage to croak "Fizz!". Doom is avoided, and the pointing finger moves on. Until the next time."
]]></description>
<dc:subject>coding-dojo agility learning-by-doing self-assessment TDD BDD training kata</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f21b61c5df8f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:coding-dojo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agility"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-assessment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:TDD"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:BDD"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:training"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:kata"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.columbia.edu/~cook/movabletype/archives/2010/03/building_a_bett.html">
    <title>Building a Better Teacher - Statistical Modeling, Causal Inference, and Social Science</title>
    <dc:date>2010-03-10T15:07:15+00:00</dc:date>
    <link>http://www.stat.columbia.edu/~cook/movabletype/archives/2010/03/building_a_bett.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Another problem I've often had (as recently as last semester!) is that my goals for students--what they're expected to be able to do when the semester is over--are often not well defined. When we don't have a sense of where we're going, our 15-week courses often fall apart somewhere around week 7 or so. But this should not be such an issue in high school."
]]></description>
<dc:subject>pedagogy teaching academia learning-by-doing advice citation-etiquette</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:05179bca71be/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pedagogy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:teaching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:academia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:advice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:citation-etiquette"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>