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    <title>PsyArXiv Preprints | How do scientists update their beliefs? An investigation of N scientists engaged in replication research</title>
    <dc:date>2019-07-29T10:56:14+00:00</dc:date>
    <link>https://psyarxiv.com/pq8m4/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[How, if at all, do scientists adjust their beliefs based on observed evidence? Few studies have examined how beliefs change over the course of a study, from developing the protocol to collecting and analyzing original data. N scientists who were contributing to one or more of six multi-lab replication projects were asked to estimate their degree of belief in the original effect and to estimate the true effect size at three timepoints: before data collection, after learning their own results, and after learning the results of all of the other laboratories contributing replication studies. We examine how beliefs changed in response to data, whether scientists are disproportionately influenced by their own study results relative to those of other labs, and whether people optimally update their prior beliefs according to the observed evidence.

]]></description>
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<item rdf:about="https://www.pnas.org/content/early/2019/01/18/1815156116">
    <title>Enhancing human learning via spaced repetition optimization | PNAS</title>
    <dc:date>2019-02-08T15:33:59+00:00</dc:date>
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    <dc:creator>Vaguery</dc:creator><description><![CDATA[Understanding human memory has been a long-standing problem in various scientific disciplines. Early works focused on characterizing human memory using small-scale controlled experiments and these empirical studies later motivated the design of spaced repetition algorithms for efficient memorization. However, current spaced repetition algorithms are rule-based heuristics with hard-coded parameters, which do not leverage the automated fine-grained monitoring and greater degree of control offered by modern online learning platforms. In this work, we develop a computational framework to derive optimal spaced repetition algorithms, specially designed to adapt to the learners’ performance. A large-scale natural experiment using data from a popular language-learning online platform provides empirical evidence that the spaced repetition algorithms derived using our framework are significantly superior to alternatives.

]]></description>
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    <title>DAVID GRAEBER / The Revolt of the Caring Classes / 2018 - YouTube</title>
    <dc:date>2018-05-13T12:48:32+00:00</dc:date>
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    <dc:creator>Vaguery</dc:creator><description><![CDATA["The financialisation of major economies since the '80s has radically changed the terms for social movements everywhere. How does one organise workplaces, for example, in societies where up to 40% of the workforce believe their jobs should not exist? David Graeber makes the case that, slowly but surely, a new form of class politics is emerging, based around recognising the centrality of meaningful 'caring labour' in creating social value. He identifies a slowly emerging rebellion of the caring classes which potentially represents just as much of a threat to financial capitalism as earlier forms of proletarian struggle did to industrial capitalism.

David Graeber is Professor of Anthropology, London School of Economics and previously Assistant Professor and Associate Professor of Anthropology at Yale and Reader in Social Anthropology at Goldsmiths, University of London. His books include The Utopia of Rules: On Technology, Stupidity, and the Secret Joys of Bureaucracy (2015) Debt: The First 5000 Years (2011) and Fragments of an Anarchist Anthropology (2004). His activism includes protests against the 3rd Summit of the Americas in Quebec City in 2001, and the 2002 World Economic Forum in New York City. Graeber was a leading figure in the Occupy Wall Street movement, and is sometimes credited with having coined the slogan, 'We are the 99 percent'.

This lecture was given at the Collège de France on the 22nd March 2018."]]></description>
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<item rdf:about="https://arxiv.org/abs/1709.06079">
    <title>[1709.06079] Orthogonal Weight Normalization: Solution to Optimization over Multiple Dependent Stiefel Manifolds in Deep Neural Networks</title>
    <dc:date>2017-09-26T14:24:01+00:00</dc:date>
    <link>https://arxiv.org/abs/1709.06079</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Orthogonal matrix has shown advantages in training Recurrent Neural Networks (RNNs), but such matrix is limited to be square for the hidden-to-hidden transformation in RNNs. In this paper, we generalize such square orthogonal matrix to orthogonal rectangular matrix and formulating this problem in feed-forward Neural Networks (FNNs) as Optimization over Multiple Dependent Stiefel Manifolds (OMDSM). We show that the rectangular orthogonal matrix can stabilize the distribution of network activations and regularize FNNs. We also propose a novel orthogonal weight normalization method to solve OMDSM. Particularly, it constructs orthogonal transformation over proxy parameters to ensure the weight matrix is orthogonal and back-propagates gradient information through the transformation during training. To guarantee stability, we minimize the distortions between proxy parameters and canonical weights over all tractable orthogonal transformations. In addition, we design an orthogonal linear module (OLM) to learn orthogonal filter banks in practice, which can be used as an alternative to standard linear module. Extensive experiments demonstrate that by simply substituting OLM for standard linear module without revising any experimental protocols, our method largely improves the performance of the state-of-the-art networks, including Inception and residual networks on CIFAR and ImageNet datasets. In particular, we have reduced the test error of wide residual network on CIFAR-100 from 20.04% to 18.61% with such simple substitution. Our code is available online for result reproduction.
]]></description>
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<item rdf:about="https://arxiv.org/abs/1705.00987">
    <title>[1705.00987] Ignorance can be evolutionarily beneficial</title>
    <dc:date>2017-09-24T12:53:56+00:00</dc:date>
    <link>https://arxiv.org/abs/1705.00987</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Information is increasingly being viewed as a resource used by organisms to increase their fitness. Indeed, it has been formally shown that there is a sensible way to assign a reproductive value to information and it is non-negative. However, all of this work assumed that information collection is cost-free. Here, we account for such a cost and provide conditions for when the reproductive value of information will be negative. In these instances, counter-intuitively, it is in the interest of the organism to remain ignorant. We link our results to empirical studies where Bayesian behaviour appears to break down in complex environments and provide an alternative explanation of lowered arousal thresholds in the evolution of sleep.
]]></description>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bounded-rationality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1705.00744">
    <title>[1705.00744] A Strategy for an Uncompromising Incremental Learner</title>
    <dc:date>2017-08-12T13:20:44+00:00</dc:date>
    <link>https://arxiv.org/abs/1705.00744</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Multi-class supervised learning systems require the knowledge of the entire range of labels they predict. Often when learnt incrementally, they suffer from catastrophic forgetting. To avoid this, generous leeways have to be made to the philosophy of incremental learning that either forces a part of the machine to not learn, or to retrain the machine again with a selection of the historic data. While these hacks work to various degrees, they do not adhere to the spirit of incremental learning. In this article, we redefine incremental learning with stringent conditions that do not allow for any undesirable relaxations and assumptions. We design a strategy involving generative models and the distillation of dark knowledge as a means of hallucinating data along with appropriate targets from past distributions. We call this technique, phantom sampling.We show that phantom sampling helps avoid catastrophic forgetting during incremental learning. Using an implementation based on deep neural networks, we demonstrate that phantom sampling dramatically avoids catastrophic forgetting. We apply these strategies to competitive multi-class incremental learning of deep neural networks. Using various benchmark datasets and through our strategy, we demonstrate that strict incremental learning could be achieved. We further put our strategy to test on challenging cases, including cross-domain increments and incrementing on a novel label space. We also propose a trivial extension to unbounded-continual learning and identify potential for future development.
]]></description>
<dc:subject>rather-interesting neural-networks learning data-synthesis hallucination to-write-about coevolution</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:8397d2a8a20c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-synthesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hallucination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:coevolution"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.columbia.edu/~gelman/research/published/objectivityr5.pdf">
    <title>Beyond subjective and objective in statistics [PDF]</title>
    <dc:date>2017-07-09T11:47:51+00:00</dc:date>
    <link>http://www.stat.columbia.edu/~gelman/research/published/objectivityr5.pdf</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Decisions in statistical data analysis are often justified, criticized, or avoided using concepts of objectivity and subjectivity. We argue that the words “objective” and “subjective” in statis- tics discourse are used in a mostly unhelpful way, and we propose to replace each of them with broader collections of attributes, with objectivity replaced by transparency, consensus, im- partiality, and correspondence to observable reality, and subjectivity replaced by awareness of multiple perspectives and context dependence. Together with stability, these make up a collection of virtues that we think is helpful in discussions of statistical foundations and practice. The advantage of these reformulations is that the replacement terms do not oppose each other and that they give more specific guidance about what statistical science strives to achieve. Instead of debating over whether a given statistical method is subjective or objective (or normatively debating the relative merits of subjectivity and objectivity in statistical practice), we can rec- ognize desirable attributes such as transparency and acknowledgment of multiple perspectives as complementary goals. We demonstrate the implications of our proposal with recent applied examples from pharmacology, election polling, and socioeconomic stratification. The aim of this paper is to push users and developers of statistical methods toward more effective use of diverse sources of information and more open acknowledgement of assumptions and goals.]]></description>
<dc:subject>statistics philosophy-of-science data-analysis looking-to-see hypothesis-testing learning to-read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0800407fc8b7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hypothesis-testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1701.09175">
    <title>[1701.09175] Skip Connections as Effective Symmetry-Breaking</title>
    <dc:date>2017-02-16T11:42:13+00:00</dc:date>
    <link>https://arxiv.org/abs/1701.09175</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Skip connections made the training of very deep neural networks possible and have become an indispendable component in a variety of neural architectures. A completely satisfactory explanation for their success remains elusive. Here, we present a novel explanation for the benefits of skip connections in training very deep neural networks. We argue that skip connections help break symmetries inherent in the loss landscapes of deep networks, leading to drastically simplified landscapes. In particular, skip connections between adjacent layers in a multilayer network break the permutation symmetry of nodes in a given layer, and the recently proposed DenseNet architecture, where each layer projects skip connections to every layer above it, also breaks the rescaling symmetry of connectivity matrices between different layers. This hypothesis is supported by evidence from a toy model with binary weights and from experiments with fully-connected networks suggesting (i) that skip connections do not necessarily improve training unless they help break symmetries and (ii) that alternative ways of breaking the symmetries also lead to significant performance improvements in training deep networks, hence there is nothing special about skip connections in this respect. We find, however, that skip connections confer additional benefits over and above symmetry-breaking, such as the ability to deal effectively with the vanishing gradients problem.
]]></description>
<dc:subject>neural-networks learning fitness-landscapes engineering-design rather-interesting symmetry nudge-targets consider:looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0bd91546add8/</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:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fitness-landscapes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symmetry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://placesjournal.org/article/a-city-is-not-a-computer/">
    <title>A City Is Not a Computer</title>
    <dc:date>2017-02-11T15:26:41+00:00</dc:date>
    <link>https://placesjournal.org/article/a-city-is-not-a-computer/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We must also recognize the shortcomings in models that presume the objectivity of urban data and conveniently delegate critical, often ethical decisions to the machine. We, humans, make urban information by various means: through sensory experience, through long-term exposure to a place, and, yes, by systematically filtering data. It’s essential to make space in our cities for those diverse methods of knowledge production. And we have to grapple with the political and ethical implications of our methods and models, embedded in all acts of planning and design. City-making is always, simultaneously, an enactment of city-knowing — which cannot be reduced to computation.]]></description>
<dc:subject>urban-planning technocracy learning modeling-is-not-mathematics social-dynamics theory-and-practice-sitting-in-a-tree to-write-about via:arthegall</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:aa7039db85f8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:urban-planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:technocracy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling-is-not-mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-dynamics"/>
	<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:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:arthegall"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1611.06928">
    <title>[1611.06928] Memory Lens: How Much Memory Does an Agent Use?</title>
    <dc:date>2016-12-12T23:55:29+00:00</dc:date>
    <link>https://arxiv.org/abs/1611.06928</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We propose a new method to study the internal memory used by reinforcement learning policies. We estimate the amount of relevant past information by estimating mutual information between behavior histories and the current action of an agent. We perform this estimation in the passive setting, that is, we do not intervene but merely observe the natural behavior of the agent. Moreover, we provide a theoretical justification for our approach by showing that it yields an implementation-independent lower bound on the minimal memory capacity of any agent that implement the observed policy. We demonstrate our approach by estimating the use of memory of DQN policies on concatenated Atari frames, demonstrating sharply different use of memory across 49 games. The study of memory as information that flows from the past to the current action opens avenues to understand and improve successful reinforcement learning algorithms.
]]></description>
<dc:subject>machine-learning agents learning information-theory to-write-about via:cshalizi complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:09634fe7c4a7/</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:agents"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1412.2309">
    <title>[1412.2309] Visual Causal Feature Learning</title>
    <dc:date>2015-11-14T13:51:08+00:00</dc:date>
    <link>http://arxiv.org/abs/1412.2309</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We provide a rigorous definition of the visual cause of a behavior that is broadly applicable to the visually driven behavior in humans, animals, neurons, robots and other perceiving systems. Our framework generalizes standard accounts of causal learning to settings in which the causal variables need to be constructed from micro-variables. We prove the Causal Coarsening Theorem, which allows us to gain causal knowledge from observational data with minimal experimental effort. The theorem provides a connection to standard inference techniques in machine learning that identify features of an image that correlate with, but may not cause, the target behavior. Finally, we propose an active learning scheme to learn a manipulator function that performs optimal manipulations on the image to automatically identify the visual cause of a target behavior. We illustrate our inference and learning algorithms in experiments based on both synthetic and real data.
]]></description>
<dc:subject>machine-learning representation ethology philosophy-of-science algorithms rather-interesting learning learning-by-watching nudge-targets consider:intermediate-representations</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:faf45da1b2b6/</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:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:ethology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-science"/>
	<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:learning"/>
	<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:intermediate-representations"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1506.06374">
    <title>[1506.06374] What can ecosystems learn? Expanding evolutionary ecology with learning theory</title>
    <dc:date>2015-11-01T09:16:31+00:00</dc:date>
    <link>http://arxiv.org/abs/1506.06374</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Understanding how the structure of community interactions is modified by coevolution is vital for understanding system responses to change at all scales. However, in absence of a group selection process, collective community behaviours cannot be organised or adapted in a Darwinian sense. An open question thus persists: are there alternative organising principles that enable us to understand how coevolution of component species creates complex collective behaviours exhibited at the community level? We address this issue using principles from connectionist learning, a discipline with well-developed theories of emergent behaviours in simple networks. We identify conditions where selection on ecological interactions is equivalent to 'unsupervised learning' (a simple type of connectionist learning) and observe that this enables communities to self organize without community-level selection. Despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal organisation that habituates to past environmental conditions and actively recalling those conditions.
]]></description>
<dc:subject>theoretical-biology ecology network-theory learning rather-interesting models-and-modes analogy philosophy-of-science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:43facdc3fb9f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:ecology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:network-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:analogy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1412.1713">
    <title>[1412.1713] Networks that learn the precise timing of event sequences</title>
    <dc:date>2015-08-24T10:58:11+00:00</dc:date>
    <link>http://arxiv.org/abs/1412.1713</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Neuronal circuits can learn and replay firing patterns evoked by sequences of sensory stimuli. After training, a brief cue can trigger a spatiotemporal pattern of neural activity similar to that evoked by a learned stimulus sequence. Network models show that such sequence learning can occur through the shaping of feedforward excitatory connectivity via long term plasticity. Previous models describe how event order can be learned, but they typically do not explain how precise timing can be recalled. We propose a mechanism for learning both the order and precise timing of event sequences. In our recurrent network model, long term plasticity leads to the learning of the sequence, while short term facilitation enables temporally precise replay of events. Learned synaptic weights between populations determine the time necessary for one population to activate another. Long term plasticity adjusts these weights so that the trained event times are matched during playback. While we chose short term facilitation as a time-tracking process, we also demonstrate that other mechanisms, such as spike rate adaptation, can fulfill this role. We also analyze the impact of trial-to-trial variability, showing how observational errors as well as neuronal noise result in variability in learned event times. The dynamics of the playback process determine how stochasticity is inherited in learned sequence timings. Future experiments that characterize such variability can therefore shed light on the neural mechanisms of sequence learning.
]]></description>
<dc:subject>neural-networks cognition learning theoretical-biology algorithms simulation biological-engineering nudge-targets consider:primitives ReQ</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1735f103f460/</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:cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:primitives"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:ReQ"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1502.02206">
    <title>[1502.02206] Learning to Search Better Than Your Teacher</title>
    <dc:date>2015-08-09T12:59:17+00:00</dc:date>
    <link>http://arxiv.org/abs/1502.02206</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Methods for learning to search for structured prediction typically imitate a reference policy, with existing theoretical guarantees demonstrating low regret compared to that reference. This is unsatisfactory in many applications where the reference policy is suboptimal and the goal of learning is to improve upon it. Can learning to search work even when the reference is poor? 
We provide a new learning to search algorithm, LOLS, which does well relative to the reference policy, but additionally guarantees low regret compared to deviations from the learned policy: a local-optimality guarantee. Consequently, LOLS can improve upon the reference policy, unlike previous algorithms. This enables us to develop structured contextual bandits, a partial information structured prediction setting with many potential applications.]]></description>
<dc:subject>learning performance-measure learning-by-watching rather-interesting machine-learning nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4c8dbf7a711e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<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:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1507.01215">
    <title>[1507.01215] Combining Models of Approximation with Partial Learning</title>
    <dc:date>2015-08-08T12:35:40+00:00</dc:date>
    <link>http://arxiv.org/abs/1507.01215</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In Gold's framework of inductive inference, the model of partial learning requires the learner to output exactly one correct index for the target object and only the target object infinitely often. Since infinitely many of the learner's hypotheses may be incorrect, it is not obvious whether a partial learner can be modifed to "approximate" the target object. 
Fulk and Jain (Approximate inference and scientific method. Information and Computation 114(2):179--191, 1994) introduced a model of approximate learning of recursive functions. The present work extends their research and solves an open problem of Fulk and Jain by showing that there is a learner which approximates and partially identifies every recursive function by outputting a sequence of hypotheses which, in addition, are also almost all finite variants of the target function. 
The subsequent study is dedicated to the question how these findings generalise to the learning of r.e. languages from positive data. Here three variants of approximate learning will be introduced and investigated with respect to the question whether they can be combined with partial learning. Following the line of Fulk and Jain's research, further investigations provide conditions under which partial language learners can eventually output only finite variants of the target language. The combinabilities of other partial learning criteria will also be briefly studied.
]]></description>
<dc:subject>learning rather-interesting representation a-different-paradigm-for-sure nudge-targets consider:analogies</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3ffed8f9a6cb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:a-different-paradigm-for-sure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:analogies"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1204.5859">
    <title>[1204.5859] On the Complexity of Finding Second-Best Abductive Explanations</title>
    <dc:date>2015-07-10T23:18:12+00:00</dc:date>
    <link>http://arxiv.org/abs/1204.5859</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[While looking for abductive explanations of a given set of manifestations, an ordering between possible solutions is often assumed. The complexity of finding/verifying optimal solutions is already known. In this paper we consider the computational complexity of finding second-best solutions. We consider different orderings, and consider also different possible definitions of what a second-best solution is.
]]></description>
<dc:subject>logic learning abduction philosophy-of-science computer-science rather-interesting nudge-targets consider:representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b828938c4ad2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:logic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:abduction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computer-science"/>
	<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:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1407.6525">
    <title>[1407.6525] Unsupervised Learning of Precise Spike Times with Membrane Potential Dependent Synaptic Plasticity</title>
    <dc:date>2014-11-05T12:45:00+00:00</dc:date>
    <link>http://arxiv.org/abs/1407.6525</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. It is, however, not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Known activity dependent synaptic plasticity rules are agnostic to the goal of learning spike times, while the existing formal and supervised methods are barely biologically plausible. Here, we propose a simple unsupervised synaptic plasticity mechanism that depends on the postsynaptic membrane potential and overcomes shortcomings of previous rules. It is derived from the basic requirement of membrane potential balance and is supported by experiments. The voltage is a global signal that makes graded and precise information about the state of the neuron available locally at the synapse. This allows plasticity to terminate when a desired state is achieved. This feature of the proposed synaptic mechanism extends the theoretical principles underlying the classical Perceptron Learning Rule to realistic spiking feed-forward networks. In particular, the sensitivity of the proposed plasticity mechanism to the membrane potential allows to introduce an adjustable margin, which makes the networks' output robust against noise. Furthermore, the stereotypic dynamics of the membrane potential close to action potentials causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce anti-Hebbian forms of spike timing dependent plasticity. For spatio-temporal input spike patterns our conceptually elementary plasticity rule achieves a surprisingly high storage capacity for spike associations, with robust memory retrieval even in the presence of input activity corrupted by noise.
]]></description>
<dc:subject>neural-networks neuroscience theoretical-biology models neural-plasticity learning the-mangle-in-practice</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d30c52adb4fd/</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:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-plasticity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1311.6041">
    <title>[1311.6041] No Free Lunch Theorem and Bayesian probability theory: two sides of the same coin. Some implications for black-box optimization and metaheuristics</title>
    <dc:date>2013-12-19T13:31:58+00:00</dc:date>
    <link>http://arxiv.org/abs/1311.6041</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Challenging optimization problems, which elude acceptable solution via conventional calculus methods, arise commonly in different areas of industrial design and practice. Hard optimization problems are those who manifest the following behavior: a) high number of independent input variables; b) very complex or irregular multi-modal fitness; c) computational expensive fitness evaluation. This paper will focus on some theoretical issues that have strong implications for practice. I will stress how an interpretation of the No Free Lunch theorem leads naturally to a general Bayesian optimization framework. The choice of a prior over the space of functions is a critical and inevitable step in every black-box optimization.
]]></description>
<dc:subject>no-free-lunch Bayesianism optimization learning philosophy-of-engineering bridge-papers</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6dd338f88497/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:no-free-lunch"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bridge-papers"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1309.3959">
    <title>[1309.3959] Bounded Confidence Opinion Dynamics in a Social Network of Bayesian Decision Makers</title>
    <dc:date>2013-09-17T17:57:59+00:00</dc:date>
    <link>http://arxiv.org/abs/1309.3959</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Bounded confidence opinion dynamics model the propagation of information in social networks. However in the existing literature, opinions are only viewed as abstract quantities without semantics rather than as part of a decision-making system. In this work, opinion dynamics are examined when agents are Bayesian decision makers that perform hypothesis testing or signal detection, and the dynamics are applied to prior probabilities of hypotheses. Bounded confidence is defined on prior probabilities through Bayes risk error divergence, the appropriate measure between priors in hypothesis testing. This definition contrasts with the measure used between opinions in standard models: absolute error. It is shown that the rapid convergence of prior probabilities to a small number of limiting values is similar to that seen in the standard Krause-Hegselmann model. The most interesting finding in this work is that the number of these limiting values and the time to convergence changes with the signal-to-noise ratio in the detection task. The number of final values or clusters is maximal at intermediate signal-to-noise ratios, suggesting that the most contentious issues lead to the largest number of factions. It is at these same intermediate signal-to-noise ratios at which the degradation in detection performance of the aggregate vote of the decision makers is greatest in comparison to the Bayes optimal detection performance.
]]></description>
<dc:subject>social-networks collective-intelligence learning context-as-a-feature nudge-targets interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2edb17d3e38b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:context-as-a-feature"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1209.4825">
    <title>[1209.4825] Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data</title>
    <dc:date>2013-07-21T14:48:59+00:00</dc:date>
    <link>http://arxiv.org/abs/1209.4825</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditioned on a particular target object. We present a general kernel framework for learning conditional rankings from various types of relational data, where rankings can be conditioned on unseen data objects. We propose efficient algorithms for conditional ranking by optimizing squared regression and ranking loss functions. We show theoretically, that learning with the ranking loss is likely to generalize better than with the regression loss. Further, we prove that symmetry or reciprocity properties of relations can be efficiently enforced in the learned models. Experiments on synthetic and real-world data illustrate that the proposed methods deliver state-of-the-art performance in terms of predictive power and computational efficiency. Moreover, we also show empirically that incorporating symmetry or reciprocity properties can improve the generalization performance.
]]></description>
<dc:subject>learning ranking performance-measure nudge-targets horse-races</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3765f095a0ae/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:ranking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:horse-races"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1303.3440">
    <title>[1303.3440] Towards a Synergy-based Approach to Measuring Information Modification</title>
    <dc:date>2013-04-08T12:32:26+00:00</dc:date>
    <link>http://arxiv.org/abs/1303.3440</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Distributed computation in artificial life and complex systems is often described in terms of component operations on information: information storage, transfer and modification. Information modification remains poorly described however, with the popularly-understood examples of glider and particle collisions in cellular automata being only quantitatively identified to date using a heuristic (separable information) rather than a proper information-theoretic measure. We outline how a recently-introduced axiomatic framework for measuring information redundancy and synergy, called partial information decomposition, can be applied to a perspective of distributed computation in order to quantify component operations on information. Using this framework, we propose a new measure of information modification that captures the intuitive understanding of information modification events as those involving interactions between two or more information sources. We also consider how the local dynamics of information modification in space and time could be measured, and suggest a new axiom that redundancy measures would need to meet in order to make such local measurements. Finally, we evaluate the potential for existing redundancy measures to meet this localizability axiom.]]></description>
<dc:subject>artificial-life agent-based learning information-theory philosophy-of-science complexology collective-intelligence nudge-targets performance-measure</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:24640d9cd4ff/</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:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<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:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1301.3545">
    <title>[1301.3545] Metric-Free Natural Gradient for Joint-Training of Boltzmann Machines</title>
    <dc:date>2013-03-10T11:39:24+00:00</dc:date>
    <link>http://arxiv.org/abs/1301.3545</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This paper introduces the Metric-Free Natural Gradient (MFNG) algorithm for training Boltzmann Machines. Similar in spirit to the Hessian-Free method of Martens [8], our algorithm belongs to the family of truncated Newton methods and exploits an efficient matrix-vector product to avoid explicitely storing the natural gradient metric $L$. This metric is shown to be the expected second derivative of the log-partition function (under the model distribution), or equivalently, the variance of the vector of partial derivatives of the energy function. We evaluate our method on the task of joint-training a 3-layer Deep Boltzmann Machine and show that MFNG does indeed have faster per-epoch convergence compared to Stochastic Maximum Likelihood with centering, though wall-clock performance is currently not competitive.]]></description>
<dc:subject>algorithms learning optimization minimization performance-measure nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2c9544c46ee5/</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:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:minimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1302.4389">
    <title>[1302.4389] Maxout Networks</title>
    <dc:date>2013-03-08T04:53:17+00:00</dc:date>
    <link>http://arxiv.org/abs/1302.4389</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called dropout. We define a simple new model called maxout (so named because its output is the max of a set of inputs, and because it is a natural companion to dropout) designed to both facilitate optimization by dropout and improve the accuracy of dropout's fast approximate model averaging technique. We empirically verify that the model successfully accomplishes both of these tasks. We use maxout and dropout to demonstrate state of the art classification performance on four benchmark datasets: MNIST, CIFAR-10, CIFAR-100, and SVHN.]]></description>
<dc:subject>models algorithms learning aggregation neural-networks nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1a1f91db833d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1209.3818">
    <title>[1209.3818] On the structure of learning agents</title>
    <dc:date>2012-09-23T11:37:07+00:00</dc:date>
    <link>http://arxiv.org/abs/1209.3818</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["This paper presents the thesis that all learning agents of finite information size are limited by their informational structure in what goals they can efficiently learn to achieve in a very complex environment. The thesis implies that there is no efficient universal learning algorithm. An agent can go past the learning limits imposed by its structure only by slow evolutionary change or blind search which in a very complex environment can only give an agent an inefficient universal learning capability that can work only in evolutionary timescales or improbable luck."]]></description>
<dc:subject>no-free-lunch agents learning philosophy pragmatism-by-hints</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:361454611f84/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:no-free-lunch"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agents"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pragmatism-by-hints"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1204.3678">
    <title>[1204.3678] Crowd Memory: Learning in the Collective</title>
    <dc:date>2012-04-21T13:04:27+00:00</dc:date>
    <link>http://arxiv.org/abs/1204.3678</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Crowd algorithms often assume workers are inexperienced and thus fail to adapt as workers in the crowd learn a task. These assumptions fundamentally limit the types of tasks that systems based on such algorithms can handle. This paper explores how the crowd learns and remembers over time in the context of human computation, and how more realistic assumptions of worker experience may be used when designing new systems. We first demonstrate that the crowd can recall information over time and discuss possible implications of crowd memory in the design of crowd algorithms. We then explore crowd learning during a continuous control task. Recent systems are able to disguise dynamic groups of workers as crowd agents to support continuous tasks, but have not yet considered how such agents are able to learn over time. We show, using a real-time gaming setting, that crowd agents can learn over time, and `remember' by passing strategies from one generation of workers to the next, despite high turnover rates in the workers comprising them. We conclude with a discussion of future research directions for crowd memory and learning."]]></description>
<dc:subject>crowdsourcing learning agent-based collective-intelligence memory nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:87bbc9903498/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:crowdsourcing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:memory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1203.0879">
    <title>[1203.0879] Designing and using prior knowledge for phase retrieval</title>
    <dc:date>2012-03-18T09:56:28+00:00</dc:date>
    <link>http://arxiv.org/abs/1203.0879</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["In this work we develop an algorithm for signal reconstruction from the magnitude of its Fourier transform in a situation where some (non-zero) parts of the sought signal are known. Although our method does not assume that the known part comprises the boundary of the sought signal, this is often the case in microscopy: a specimen is placed inside a known mask, which can be thought of as a known light source that surrounds the unknown signal. Therefore, in the past, several algorithms were suggested that solve the phase retrieval problem assuming known boundary values. Unlike our method, these methods do rely on the fact that the known part is on the boundary. Besides the reconstruction method we give an explanation of the phenomena observed in previous work: the reconstruction is much faster when there is more energy concentrated in the known part. Quite surprisingly, this can be explained using our previous results on phase retrieval with approximately known Fourier phase."]]></description>
<dc:subject>image-analysis image-processing learning inverse-problems algorithms nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:14a58901fbc9/</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-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inverse-problems"/>
	<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://arxiv.org/abs/1106.1796">
    <title>[1106.1796] Accelerating Reinforcement Learning by Composing Solutions of Automatically Identified Subtasks</title>
    <dc:date>2011-10-04T11:48:29+00:00</dc:date>
    <link>http://arxiv.org/abs/1106.1796</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["This paper discusses a system that accelerates reinforcement learning by using transfer from related tasks. Without such transfer, even if two tasks are very similar at some abstract level, an extensive re-learning effort is required. The system achieves much of its power by transferring parts of previously learned solutions rather than a single complete solution. The system exploits strong features in the multi-dimensional function produced by reinforcement learning in solving a particular task. These features are stable and easy to recognize early in the learning process. They generate a partitioning of the state space and thus the function. The partition is represented as a graph. This is used to index and compose functions stored in a case base to form a close approximation to the solution of the new task. Experiments demonstrate that function composition often produces more than an order of magnitude increase in learning rate compared to a basic reinforcement learning algorithm."]]></description>
<dc:subject>algorithms learning problem-solving decomposition specification nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b07ed43ea2b3/</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:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:problem-solving"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:decomposition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:specification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://icos.umich.edu/lecture-2010-11-05">
    <title>Michael Cohen, University of Michigan | Interdisciplinary Committee on Organizational Studies</title>
    <dc:date>2011-05-26T13:40:38+00:00</dc:date>
    <link>http://icos.umich.edu/lecture-2010-11-05</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Routine Matters: New Foundations for the Study of Recurring Organizational Action Patterns]]></description>
<dc:subject>Michael-Cohen habit Pragmatism lecture organizational-behavior cognition learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:db38582493ef/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Michael-Cohen"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:habit"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Pragmatism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:lecture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:organizational-behavior"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://des.emory.edu/mfp/tt8.html">
    <title>James on Habit</title>
    <dc:date>2011-05-16T11:34:50+00:00</dc:date>
    <link>http://des.emory.edu/mfp/tt8.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["…Keep the faculty of effort alive in you by a little gratuitous exercise every day. That is, be systematically heroic in little unnecessary points, do every day or two something for no other reason than its difficulty, so that, when the hour of dire need draws nigh, it may find you not unnerved and untrained to stand the test."]]></description>
<dc:subject>habit psychology sociology William-James advice learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:75a646a5335a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:habit"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:William-James"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:advice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://en.wikibooks.org/wiki/R_Programming">
    <title>R Programming - Wikibooks, collection of open-content textbooks</title>
    <dc:date>2010-05-14T11:41:23+00:00</dc:date>
    <link>http://en.wikibooks.org/wiki/R_Programming</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["This is a guide to the R programming language."
]]></description>
<dc:subject>R R-language documentation learning open-source statistics programming</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2810d496c71c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:R-language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:documentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:open-source"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:programming"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://edgeperspectives.typepad.com/edge_perspectives/2010/01/reshaping-relationships-through-passion.html">
    <title>Edge Perspectives with John Hagel: Reshaping Relationships through Passion</title>
    <dc:date>2010-01-30T22:41:22+00:00</dc:date>
    <link>http://edgeperspectives.typepad.com/edge_perspectives/2010/01/reshaping-relationships-through-passion.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["The Big Shift suggests we are moving away from a world where stocks of knowledge and short-lived transactions are the key to success. In its place, we find a world where participation in many, diverse flows of knowledge and long-term, trust-based relationships determine success. In this new world, shy people can be at a significant disadvantage. We run the risk of becoming increasingly stressed and marginalized by the extroverts who welcome the opportunity to broaden and deepen relationships. They thrive in crowded rooms while we are deeply uncomfortable with exposing and sharing."
]]></description>
<dc:subject>social-norms learning network-culture stock-and-flow cultural-dynamics knowledge collaboration trust</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:431bf3c13d12/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-norms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:network-culture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:stock-and-flow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cultural-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:knowledge"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collaboration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:trust"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.zenmoments.org/my-favorite-liar/">
    <title>My Favorite Liar | Zen Moments</title>
    <dc:date>2009-11-28T02:01:26+00:00</dc:date>
    <link>http://www.zenmoments.org/my-favorite-liar/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Brilliant … but what made Dr. K’s technique most insidiously evil and genius was, during the most technically difficult lecture of the entire quarter, there was no lie. At the end of the lecture in which he was not called on any lie, he offered the same challenge to work through the notes; on the following Monday, he fielded our theories for what the falsehood might be (and shooting them down “no, in fact that is true – look at “) for almost ten minutes before he finally revealed: “Do you remember the first lecture – how I said that ‘every lecture has a lie?’”"
]]></description>
<dc:subject>critical-thinking pedagogy liars education psychology learning teaching leadership</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:33a8cc38253c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:critical-thinking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pedagogy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:liars"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:teaching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:leadership"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://openresearch.sebpaquet.net/2009/10/fate-of-incompetent-teacher-in-youtube.html">
    <title>Seb's Open Research: The Fate of the Incompetent Teacher in the YouTube Era</title>
    <dc:date>2009-11-03T16:28:16+00:00</dc:date>
    <link>http://openresearch.sebpaquet.net/2009/10/fate-of-incompetent-teacher-in-youtube.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["How fast is this going to happen? Well, Khan is already becoming famous. Last year CNN gave him airtime to explain the financial crisis. Why him, and not an economics Ph.D. type, you ask? Because he is understandable, and because some genius at CNN figured out that at least some of their viewers were able and willing to learn a little bit in order to understand what is going on."
]]></description>
<dc:subject>pedagogy web2.0 disintermediation education academia YouTube learning teaching distance science2.0</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c346fa0ec45c/</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:web2.0"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:disintermediation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:academia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:YouTube"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:teaching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:distance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:science2.0"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.i-journals.org/ejs/viewarticle.php?id=485&amp;layout=abstract">
    <title>Electronic Journal of Statistics - Vol. 3 (2009)</title>
    <dc:date>2009-11-03T13:06:44+00:00</dc:date>
    <link>http://www.i-journals.org/ejs/viewarticle.php?id=485&amp;layout=abstract</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["An appendix sketches connections between these results and the replicator dynamics of evolutionary theory."
]]></description>
<dc:subject>Bayesianism learning models model-discovery evolutionary-algorithms</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a1452293df36/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:model-discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.kamalnigam.com/papers/cotrain-CIKM00.pdf">
    <title>Analyzing the effectiveness and applicability of co-training</title>
    <dc:date>2009-09-28T12:00:00+00:00</dc:date>
    <link>http://www.kamalnigam.com/papers/cotrain-CIKM00.pdf</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Yet, the co-training algorithm in this paper also makes the same assumptions (as it too has underlying naive Bayes clas- sifiers), but does not suffer from the violations. Thus we hypothesize that the co-training algorithm succeeds in part because it is more robust to the assumptions made by its underlying classifiers. This can be understood by looking at the differences in how EM and co-training use the underly- ing assumptions."
]]></description>
<dc:subject>via:cshalizi learning learning-from-watching algorithms machine-learning collaboration performance-space-analysis</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b2e87a132216/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-from-watching"/>
	<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:collaboration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-space-analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.ted.com/index.php/talks/elizabeth_gilbert_on_genius.html">
    <title>Elizabeth Gilbert on nurturing creativity | Video on TED.com</title>
    <dc:date>2009-09-12T12:24:35+00:00</dc:date>
    <link>http://www.ted.com/index.php/talks/elizabeth_gilbert_on_genius.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In case you, reader, cannot see where she is pointing into the corner at her genius: she is pointing at her context, her network, her friends and learning and colleagues and enemies, what she has read and who she has spoken to, what she has done and never noticed, and what she has heard and never noticed and who she has met and never noticed. You are the genius of others.
]]></description>
<dc:subject>collaboration tacit-knowledge learning making art creativity manic-depression-is-not-required</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c83351f92d82/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collaboration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tacit-knowledge"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:creativity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:manic-depression-is-not-required"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://magazine.jhu.edu/2009/08/the-autodidact-course-catalog/">
    <title>Johns Hopkins Magazine – The Autodidact Course Catalog</title>
    <dc:date>2009-09-07T16:37:34+00:00</dc:date>
    <link>http://magazine.jhu.edu/2009/08/the-autodidact-course-catalog/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["One would be hard-pressed to disapprove of autodidacticism. Consider a list of notable alumni from the academy of the self-taught: René Descartes, Benjamin Franklin, Abraham Lincoln, William Blake. Michael Faraday apprenticed himself to a bookseller and read everything he could before going on to figure out electromagnetism. August Wilson schooled himself at the Carnegie Library in Pittsburgh after dropping out of the ninth grade. Arnold Schoenberg claimed to be an autodidact, and who are we to dispute it? Frank Zappa advised, “Forget about the senior prom and go to the library and educate yourself, if you’ve got any guts.” Hear, hear. (Though if the prom band is playing Frank Zappa songs, we’re donning a powder-blue brocade tux and we’re going.)"
]]></description>
<dc:subject>autodidact generalism continuing-education learning pedagogy independence reading books teaching to-read</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:137364c3821e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:autodidact"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generalism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:continuing-education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pedagogy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:independence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reading"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:teaching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.boardgamegeek.com/wiki/page/Pin:_The_Games_Collection">
    <title>Pin: The Games Collection | Wiki | BoardGameGeek</title>
    <dc:date>2009-06-25T10:59:15+00:00</dc:date>
    <link>http://www.boardgamegeek.com/wiki/page/Pin:_The_Games_Collection</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["This series of games has a consistent size and format, and any four will fit neatly into The Games Collection Stand (Pin's part number 02705)."
]]></description>
<dc:subject>games thinking learning learning-by-doing Nudge</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:235729efe967/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:thinking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Nudge"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://tschofen.wordpress.com/2009/05/05/unstable-ground/">
    <title>Unstable ground « Thinking Out Loud</title>
    <dc:date>2009-05-18T20:54:11+00:00</dc:date>
    <link>http://tschofen.wordpress.com/2009/05/05/unstable-ground/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["And I worry that the idea that learning in relation to history can easily be kept within some type of bounds implies, to a degree, that the importance of history is its factual content. Generations of captive history students, face-down and drooling on their desks, indicate that approaches of this nature are not only unfortunately limited, but also a fatal blow to any intrinsic interest in examining historical/cultural change."
]]></description>
<dc:subject>via:tsuomela history pedagogy learning-by-doing learning cultural-norms memory pragmatism</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:45357f2a4f9b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:tsuomela"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:history"/>
	<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:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cultural-norms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:memory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pragmatism"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://shoooes.net/">
    <title>Shoes • Colorful programs for Mac OS X, Linux and Windows</title>
    <dc:date>2009-04-11T21:37:48+00:00</dc:date>
    <link>http://shoooes.net/</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>design programming library learning GUI framework learning-by-doing</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5590b5adc723/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:library"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:GUI"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:framework"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://blog.objectmentor.com/articles/2009/04/01/master-craftsman-teams">
    <title>Master Craftsman Teams.</title>
    <dc:date>2009-04-05T11:26:50+00:00</dc:date>
    <link>http://blog.objectmentor.com/articles/2009/04/01/master-craftsman-teams</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Why should a young aspiring software professional spend four years and $200K+ to attend an institution that will teach them less about their chosen profession than 3 months of working on a real project with talented mentors? Indeed, why should employers pay $50K for undertrained programmers who are sure to make horrific messes for the next three years of their career?

Consider instead a team of craftspeople. At the center of this team is a master programmer. This is someone who has been programming for two decades or more. This person understand systems at a gut level, and can quickly make technical judgements without agonizing over them. Such a person can direct a team with the kind of calm confidence that only comes with years of experience and seasoning."
]]></description>
<dc:subject>academia training pedagogy guild computer-science-is-not-software-development programming development engineering learning craftsmanship</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e1735f716408/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:academia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:training"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pedagogy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:guild"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computer-science-is-not-software-development"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:development"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:craftsmanship"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.siu.edu/~siupress/deweycollection.htm">
    <title>The Collected Works of John Dewey</title>
    <dc:date>2009-03-14T21:42:29+00:00</dc:date>
    <link>http://www.siu.edu/~siupress/deweycollection.htm</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[If you want to buy me a present, buy me this. The whole set.
]]></description>
<dc:subject>John-Dewey Dewey philosophy collection pragmatics education craft learning books expensive-but-desired</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a21a1942dea9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:John-Dewey"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Dewey"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pragmatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:craft"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:expensive-but-desired"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.hss.cmu.edu/philosophy/faculty-kelly.php">
    <title>Carnegie Mellon Department Of Philosophy: Kevin Kelly</title>
    <dc:date>2009-02-25T16:20:14+00:00</dc:date>
    <link>http://www.hss.cmu.edu/philosophy/faculty-kelly.php</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["I am mainly interested in how scientific method could possibly lead us to true generalizations about Nature; generalizations that extend infinitely beyond our current, finite perspective. Standard philosophy of science sidesteps this question by asking, instead, about the meanings of "justification" and "rationality" a different matter entirely. I put the former question front and center, so that methodological normativity must be traced back to truth-finding efficacy, rather than to sociological generalizations about scientific practice. In this respect, my approach to epistemology closely parallels work in theoretical computer science and the foundations of mathematics, in which the central question is existence of a reliable procedure for finding the right answer to a question. The shift in emphasis results in a fresh, new perspective on a number of standard issues in epistemology and the philosophy of science, such as:..."
]]></description>
<dc:subject>via:arthegall philosophy-of-science philosophy epistemology methodologies modeling learning hypotheses</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:715fda9cee39/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:arthegall"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:epistemology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:methodologies"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hypotheses"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://suburbdad.blogspot.com/2009/02/error-and-failure.html">
    <title>Confessions of a Community College Dean: Error and Failure</title>
    <dc:date>2009-02-21T19:00:04+00:00</dc:date>
    <link>http://suburbdad.blogspot.com/2009/02/error-and-failure.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Grad school was even worse. At that level, a self-selected bunch of failure avoiders competed for faculty approval in a pretty airless environment for years. By the end, it took an act of will just to put together a declarative sentence. The most damning insult in grad school was “naive,” which was typically applied to anyone who actually made some sort of positive claim. (“Naive realism” was the worst, since it implied the unforgivable sin of claiming to actually know something about something.) Self-doubt can be taught.

In grad school, too, I recall the faculty being perplexed as to why so many doctoral students seemed oddly hesitant and overly deferential during oral exams. At one panel of grad student papers, I recall noticing that every single grad student started her presentation with “this is a work in progress.” Translated, that means “please don't attack me.” These habits are learned...."
]]></description>
<dc:subject>academia culture learning self-image ego social-dynamics hierarchy anthropology rebellion</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d93a077d9818/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:academia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:culture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-image"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:ego"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hierarchy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:anthropology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rebellion"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.buccaneerscholar.com/blog/archives/3">
    <title>How I Learn Stuff » Blog Archive » Buccaneer-Scholar Defined</title>
    <dc:date>2009-02-21T12:59:17+00:00</dc:date>
    <link>http://www.buccaneerscholar.com/blog/archives/3</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["A buccaneer-scholar is anyone whose love of learning is not muzzled, yoked or shackled by any institution or authority; whose mind is driven to wander and find its own voice and place in the world."
]]></description>
<dc:subject>learning scholarship independence community</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e989a2b6bbec/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:scholarship"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:independence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:community"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://chronicle.com/review/brainstorm/fendrich/why-major-in-painting?utm_source=cr&amp;utm_medium=en">
    <title>Brainstorm: Why Major in Painting? - Chronicle.com</title>
    <dc:date>2008-06-25T12:25:07+00:00</dc:date>
    <link>http://chronicle.com/review/brainstorm/fendrich/why-major-in-painting?utm_source=cr&amp;utm_medium=en</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[True for nearly every discipline beside "painting" as well. Including the ones where one may be "more successful". I know a lot of useless computer scientists, for example.
]]></description>
<dc:subject>pedagogy academia worklife learning-by-doing learning suitedness advice</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:422c137e7e5c/</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:academia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:worklife"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:suitedness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:advice"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.codinghorror.com/blog/archives/001138.html">
    <title>Coding Horror: The Ultimate Code Kata</title>
    <dc:date>2008-06-25T12:21:01+00:00</dc:date>
    <link>http://www.codinghorror.com/blog/archives/001138.html</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>advice blogging career worklife development education engineering focus learning philosophy practice productivity training kata</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7a6ba2a79c16/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:advice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:blogging"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:career"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:worklife"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:development"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:focus"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:productivity"/>
	<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.hyperorg.com/backissues/joho-may30-08.html">
    <title>JOHO - May 30, 2008</title>
    <dc:date>2008-06-02T00:13:16+00:00</dc:date>
    <link>http://www.hyperorg.com/backissues/joho-may30-08.html</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>learning invention innovation design insight learning-by-doing history</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7f532fe7181c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:invention"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:innovation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:insight"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:history"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.ailab.si/orange/">
    <title>Orange</title>
    <dc:date>2008-04-28T14:02:53+00:00</dc:date>
    <link>http://www.ailab.si/orange/</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>visualization visual-programming algorithms analysis analytics classification data-mining learning machine-learning mining modeling prediction Python open-source GPL</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:81ee06cec103/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:visual-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:analytics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:open-source"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:GPL"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.lifehack.org/articles/lifestyle/how-to-be-an-expert-and-find-one-if-youre-not.html">
    <title>How to Be an Expert (and Find One if You’re Not) - Lifehack.org</title>
    <dc:date>2008-04-06T11:09:09+00:00</dc:date>
    <link>http://www.lifehack.org/articles/lifestyle/how-to-be-an-expert-and-find-one-if-youre-not.html</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>expertise learning personal-brand career worklife</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:fb3b23be52c8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:expertise"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:personal-brand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:career"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:worklife"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://b-course.cs.helsinki.fi/obc/">
    <title>B-Course</title>
    <dc:date>2008-03-27T02:03:42+00:00</dc:date>
    <link>http://b-course.cs.helsinki.fi/obc/</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>via:arthegall Bayesian classification machine-learning statistics web applications learning software service tutorial</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:fbd81c4cc4e2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:arthegall"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Bayesian"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:web"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:applications"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:software"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:service"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.technologyreview.com/blog/boyden/21925/">
    <title>Technology Review: Blogs: Ed Boyden's blog: How to Think</title>
    <dc:date>2008-03-22T13:22:43+00:00</dc:date>
    <link>http://www.technologyreview.com/blog/boyden/21925/</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>via:mitten via:vielmetti advice learning-by-doing self-help cognition worklife planning goals GTD knowledge learning management tea</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6e511acf5367/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:mitten"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:vielmetti"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:advice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-help"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:worklife"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:goals"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:GTD"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:knowledge"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:management"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tea"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.overcomingbias.com/2008/03/rationality-q-1.html">
    <title>Overcoming Bias: Rationality Quotes 11</title>
    <dc:date>2008-03-05T13:37:09+00:00</dc:date>
    <link>http://www.overcomingbias.com/2008/03/rationality-q-1.html</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>quotes philosophy science learning psychology amusing</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0e49eaab4b5b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:quotes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:amusing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://bentrem.sycks.net/gnodal/index.html">
    <title>Participatory Deliberation - HomePage</title>
    <dc:date>2008-02-16T11:34:10+00:00</dc:date>
    <link>http://bentrem.sycks.net/gnodal/index.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["I have for all my mature life been impressed by people's tenacity, and in no specific more than discussion, whether in electronic forums or newspapers' letters to the editor..."
]]></description>
<dc:subject>via:vielmetti philosophy argument quotes learning dialog modeling abstraction insight</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4bbd228274c2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:vielmetti"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:argument"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:quotes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dialog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:abstraction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:insight"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mike.teczno.com/notes/art-rules.html">
    <title>immaculate heart college art department rules (tecznotes)</title>
    <dc:date>2008-02-04T12:25:55+00:00</dc:date>
    <link>http://mike.teczno.com/notes/art-rules.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[via: boingboing
]]></description>
<dc:subject>advice creativity education learning-by-doing learning pedagogy philosophy quotes</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:fa83b7ba82f9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:advice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:creativity"/>
	<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:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pedagogy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:quotes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.overcomingbias.com/2008/02/hemlock-parable.html">
    <title>Overcoming Bias: The Parable of Hemlock</title>
    <dc:date>2008-02-04T12:02:00+00:00</dc:date>
    <link>http://www.overcomingbias.com/2008/02/hemlock-parable.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Logic stays true, wherever you may go,
So logic never tells you where you live."
]]></description>
<dc:subject>reasoning philosophy statistics Bayesianism logic learning rationality</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c078aa52bee2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reasoning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:logic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rationality"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.overcomingbias.com/2008/01/beautiful-proba.html">
    <title>Overcoming Bias: Beautiful Probability</title>
    <dc:date>2008-01-17T12:58:47+00:00</dc:date>
    <link>http://www.overcomingbias.com/2008/01/beautiful-proba.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["We aren't enchanted by Bayesian methods merely because they're beautiful.  The beauty is a side effect."
]]></description>
<dc:subject>statistics probability-theory models cultural-norms probability Bayesianism frequentism experiment reasoning learning worldviews</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:20c1c1a8b6d9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:probability-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cultural-norms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:frequentism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reasoning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:worldviews"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://askpang.typepad.com/relevant_history/2003/06/word_spacing_si.html">
    <title>Relevant History: Word spacing, silent reading, and cyborgs</title>
    <dc:date>2008-01-08T22:18:20+00:00</dc:date>
    <link>http://askpang.typepad.com/relevant_history/2003/06/word_spacing_si.html</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>books history language learning medieval printing reading technology</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:30d32726f31c/</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:history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:medieval"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:printing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reading"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:technology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://openwetware.org/wiki/Main_Page">
    <title>Main Page - OpenWetWare</title>
    <dc:date>2008-01-04T12:46:07+00:00</dc:date>
    <link>http://openwetware.org/wiki/Main_Page</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>openness academia biological-engineering collaboration community education free learning science sharing social-norms cultural-norms publishing public-policy</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2727b15f3ccd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:openness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:academia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collaboration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:community"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:free"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sharing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-norms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cultural-norms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:publishing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:public-policy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.overcomingbias.com/2007/11/artificial-addi.html">
    <title>Overcoming Bias: Artificial Addition</title>
    <dc:date>2007-11-22T18:12:24+00:00</dc:date>
    <link>http://www.overcomingbias.com/2007/11/artificial-addi.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["When the basic problem is your ignorance, clever strategies for bypassing your ignorance lead to shooting yourself in the foot"
]]></description>
<dc:subject>analogy computer-science artificial-intelligence AI learning philosophy humor advice</dc:subject>
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<item rdf:about="http://laudatortemporisacti.blogspot.com/2007/11/abundance-of-books.html">
    <title>Laudator Temporis Acti: An Abundance of Books</title>
    <dc:date>2007-11-22T13:04:05+00:00</dc:date>
    <link>http://laudatortemporisacti.blogspot.com/2007/11/abundance-of-books.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Books have led some to learning, and others to madness..."
]]></description>
<dc:subject>books bibliomania libraries learning quotes Petrarch Classics scholarship amateurism pomposity admonition</dc:subject>
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<item rdf:about="http://www.pedagogicalpatterns.org/">
    <title>The Pedagogical Patterns Project</title>
    <dc:date>2007-10-21T15:14:21+00:00</dc:date>
    <link>http://www.pedagogicalpatterns.org/</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>design-patterns design pedagogy learning models planning philosophy Alexandrianism</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b323ab1e501a/</dc:identifier>
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<item rdf:about="http://www.sciencemusings.com/2007/07/tyranny-of-discontinuous-mind.html">
    <title>Science Musings by Chet Raymo</title>
    <dc:date>2007-07-08T14:51:45+00:00</dc:date>
    <link>http://www.sciencemusings.com/2007/07/tyranny-of-discontinuous-mind.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["When the mind fixates on absolute discontinuities, mischief is often in the offing..."
]]></description>
<dc:subject>heuristics biology learning classification advice Richard-Dawkins gray-area</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9fa27acfb0f2/</dc:identifier>
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<item rdf:about="http://www.stoweboyd.com/message/2007/06/steve_rubel_bec.html">
    <title>/Message: Steve Rubel Becomes Another Attention Economist</title>
    <dc:date>2007-06-13T18:45:38+00:00</dc:date>
    <link>http://www.stoweboyd.com/message/2007/06/steve_rubel_bec.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["We need to unfocus, to rely more on the network or tribe to surface things of importance, and remain open to new opportunities: these are potentially more important than the work on the desk. Don't sharpen the knife too much."
]]></description>
<dc:subject>via:vielmetti flow GTD worklife information-overload learning cultural-norms collaboration attention productivity</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:cdb75f720c4a/</dc:identifier>
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</item>
<item rdf:about="http://www.scu.edu.au/schools/gcm/ar/arhome.html">
    <title>Action research resources</title>
    <dc:date>2007-06-08T23:09:07+00:00</dc:date>
    <link>http://www.scu.edu.au/schools/gcm/ar/arhome.html</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>via:tsuomela learning-by-doing pedagogy education learning teaching</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:859eb2418e4b/</dc:identifier>
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</item>
<item rdf:about="http://cosy.cs.unicam.it/FBTC/">
    <title>FBTC2007</title>
    <dc:date>2007-05-03T23:56:02+00:00</dc:date>
    <link>http://cosy.cs.unicam.it/FBTC/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Probably won't be able to attend.
]]></description>
<dc:subject>CFP call-for-papers concurrency computer-science biology simulation artificial-life ALife models learning theoretical-biology</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2e8f13cf5880/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:ALife"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
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<item rdf:about="http://lifehacker.com/software/quicksilver/hack-attack-a-beginners-guide-to-quicksilver-247129.php">
    <title>Hack Attack: A beginner's guide to Quicksilver - Lifehacker</title>
    <dc:date>2007-04-19T12:12:39+00:00</dc:date>
    <link>http://lifehacker.com/software/quicksilver/hack-attack-a-beginners-guide-to-quicksilver-247129.php</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Been using Quicksilver for months; time to take it to the next level.
]]></description>
<dc:subject>quicksilver MacOS productivity software learning hack Apple utility</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:34aa2cfa81df/</dc:identifier>
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</item>
<item rdf:about="http://hunch.net/?p=259">
    <title>Machine Learning (Theory) » Contextual Scaling</title>
    <dc:date>2007-04-15T11:36:31+00:00</dc:date>
    <link>http://hunch.net/?p=259</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Half of the inevitable cycle of lumping and splitting, in the context of machine learning....
]]></description>
<dc:subject>machine-learning data statistics visualization models automation learning</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e5a5faf0d095/</dc:identifier>
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</item>
<item rdf:about="http://ideamatt.blogspot.com/2007/04/key-to-continuous-learning-keep.html">
    <title>Matt's Idea Blog: A key to continuous learning: Keep a decision log</title>
    <dc:date>2007-04-09T17:08:27+00:00</dc:date>
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    <dc:creator>Vaguery</dc:creator><dc:subject>advice GTD learning-by-doing learning self-help decision-making management kaizen productivity</dc:subject>
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<item rdf:about="http://www.stat.columbia.edu/~cook/movabletype/archives/2007/04/nassim_talebs_t.html">
    <title>Statistical Modeling, Causal Inference, and Social Science: Nassim Taleb's &quot;The Black Swan&quot;</title>
    <dc:date>2007-04-09T02:34:54+00:00</dc:date>
    <link>http://www.stat.columbia.edu/~cook/movabletype/archives/2007/04/nassim_talebs_t.html</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>anomalies philosophy science statistics learning prediction planning book review</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0a9d254955de/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:review"/>
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