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    <title>What does &quot;&gt;&quot; really mean?</title>
    <dc:date>2017-09-27T11:57:34+00:00</dc:date>
    <link>https://publications.mfo.de/handle/mfo/430</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This Snapshot is about the generalization of ">" from ordinary numbers to so-called fields. At the end, I will touch on some ideas in recent research.
]]></description>
<dc:subject>mathematics generalization rather-interesting summary</dc:subject>
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<dc:identifier>https://pinboard.in/u:Vaguery/b:69232ae0e9e5/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1504.02462">
    <title>[1504.02462] A Group Theoretic Perspective on Unsupervised Deep Learning</title>
    <dc:date>2015-06-27T13:30:34+00:00</dc:date>
    <link>http://arxiv.org/abs/1504.02462</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Why does Deep Learning work? What representations does it capture? How do higher-order representations emerge? We study these questions from the perspective of group theory, thereby opening a new approach towards a theory of Deep learning. 
One factor behind the recent resurgence of the subject is a key algorithmic step called {\em pretraining}: first search for a good generative model for the input samples, and repeat the process one layer at a time. We show deeper implications of this simple principle, by establishing a connection with the interplay of orbits and stabilizers of group actions. Although the neural networks themselves may not form groups, we show the existence of {\em shadow} groups whose elements serve as close approximations. 
Over the shadow groups, the pre-training step, originally introduced as a mechanism to better initialize a network, becomes equivalent to a search for features with minimal orbits. Intuitively, these features are in a way the {\em simplest}. Which explains why a deep learning network learns simple features first. Next, we show how the same principle, when repeated in the deeper layers, can capture higher order representations, and why representation complexity increases as the layers get deeper.
]]></description>
<dc:subject>deep-learning generative-models group-theory pre-training rather-interesting summary</dc:subject>
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<dc:identifier>https://pinboard.in/u:Vaguery/b:10a046f3cd7e/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1408.4551">
    <title>[1408.4551] Dimensionality Reduction of Affine Variational Inequalities Using Random Projections</title>
    <dc:date>2014-10-11T11:11:26+00:00</dc:date>
    <link>http://arxiv.org/abs/1408.4551</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We present a method for dimensionality reduction of an affine variational inequality (AVI) defined over a compact feasible region. Centered around the Johnson Lindenstrauss lemma, our method is a randomized algorithm that produces with high probability an approximate solution for the given AVI by solving a lower-dimensional AVI. The algorithm allows the lower dimension to be chosen based on the quality of approximation desired. The algorithm can also be used as a subroutine in an exact algorithm for generating an initial point close to the solution. The lower-dimensional AVI is obtained by appropriately projecting the original AVI on a randomly chosen subspace. The lower-dimensional AVI is solved using standard solvers and from this solution an approximate solution to the original AVI is recovered through an inexpensive process. Our numerical experiments corroborate the theoretical results and validate that the algorithm provides a good approximation at low dimensions and substantial savings in time for an exact solution.
]]></description>
<dc:subject>dimensional-reduction projection-methods algorithms statistics summary models randomized-algorithms nudge-targets machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:42b1056fdf9a/</dc:identifier>
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    <title>[1302.2645] Geometrical complexity of data approximators</title>
    <dc:date>2013-05-25T11:36:22+00:00</dc:date>
    <link>http://arxiv.org/abs/1302.2645</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[There are many methods developed to approximate a cloud of vectors embedded in high-dimensional space by simpler objects: starting from principal points and linear manifolds to self-organizing maps, neural gas, elastic maps, various types of principal curves and principal trees, and so on. For each type of approximators the measure of the approximator complexity was developed too. These measures are necessary to find the balance between accuracy and complexity and to define the optimal approximations of a given type. We propose a measure of complexity (geometrical complexity) which is applicable to approximators of several types and which allows comparing data approximations of different types.
]]></description>
<dc:subject>algorithms approximation computational-geometry computational-complexity nudge-targets summary</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6776f6500588/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1304.6023">
    <title>[1304.6023] Spaces, Trees and Colors: The Algorithmic Landscape of Document Retrieval on Sequences</title>
    <dc:date>2013-05-24T11:44:27+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.6023</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Document retrieval is one of the best established information retrieval activities since the sixties, pervading all search engines. Its aim is to obtain, from a collection of text documents, those most relevant to a pattern query. Current technology is mostly oriented to "natural language" text collections, where inverted indices are the preferred solution. As successful as this paradigm has been, it fails to properly handle some East Asian languages and other scenarios where the "natural language" assumptions do not hold. In this survey we cover the recent research in extending the document retrieval techniques to a broader class of sequence collections, which has applications bioinformatics, data and Web mining, chemoinformatics, software engineering, multimedia information retrieval, and many others. We focus on the algorithmic aspects of the techniques, uncovering a rich world of relations between document retrieval challenges and fundamental problems on trees, strings, range queries, discrete geometry, and others.
]]></description>
<dc:subject>review database digital-humanities indexing algorithms nudge-targets summary updated</dc:subject>
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    <title>The Rude Pundit</title>
    <dc:date>2012-06-29T11:46:30+00:00</dc:date>
    <link>http://rudepundit.blogspot.com/2012/06/your-daily-example-of-republican.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["And there's everything you need to know about the Republican Party. "Shit happened, but so what? People were victimized, but why should we care? That was nearly forty years ago." The dementia in refusing to look backward, refusing to make up for the mistakes of the past, whether it's the Bush tax cuts or the lies that got us into war or the lies that got us into this financial crisis, makes us damned to repeat. "]]></description>
<dc:subject>summary politics Republicans</dc:subject>
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    <title>The Complete Guide to CleanTech ETFs -- Seeking Alpha</title>
    <dc:date>2009-05-24T12:13:01+00:00</dc:date>
    <link>http://seekingalpha.com/article/139174-the-complete-guide-to-cleantech-etfs?source=feed</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["For the long term investor, cleantech is one of the best sector plays out there. And the best way to play a sector is with a low-cost ETF. This guide will review all 16 cleantech ETFs traded in the US, and offer investors the information they need to add a slice of long term growth to their portfolio. Not all cleantech ETFs are made alike, and we'll show you which funds are the best bets for future returns:"
]]></description>
<dc:subject>investment finance ETFs summary lists</dc:subject>
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<item rdf:about="http://www.aaronsw.com/weblog/predatorstate">
    <title>The Predator State: A Summary (Aaron Swartz's Raw Thought)</title>
    <dc:date>2008-08-22T10:57:20+00:00</dc:date>
    <link>http://www.aaronsw.com/weblog/predatorstate</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["6: The argument for free trade comes from Ricardo's "comparative advantage" -- a clever textbook exercise, but irrelevant to the real world since it assumes constant costs. In reality, either you produce manufactured goods, in which your costs go down as you make more, or you sell off commodities, in which case your costs go up as you make more. With the former, it takes time for local industry to build up the advantage (requiring protectionism). With the latter, you end up like Mongolia, which opened up its animal husbandry market, swelling herd sizes, turning grass into permanent desert, and killing off the entire market. With no other exports, such a country is in big trouble. Ricardo was wrong: diversification, not specialization, is the way to develop -- and how every successful country has. Unfortunately, we've forced this broken system on most of the world....
]]></description>
<dc:subject>via:cshalizi economics public-policy planning America myths diversity summary books</dc:subject>
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    <title>indexed: This is what 2.0 means.</title>
    <dc:date>2008-08-17T12:29:02+00:00</dc:date>
    <link>http://indexed.blogspot.com/2008/08/this-is-what-20-means.html</link>
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    <link>http://codefluency.com/articles/2008/02/04/ruby-19-presentation/</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>Ruby language programming presentation upgrade summary</dc:subject>
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<item rdf:about="http://www.smartmobs.com/2008/02/23/why-im-hooked-on-twitter/">
    <title>Smart Mobs » Blog Archive » Why I’m hooked on Twitter</title>
    <dc:date>2008-02-26T12:18:02+00:00</dc:date>
    <link>http://www.smartmobs.com/2008/02/23/why-im-hooked-on-twitter/</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>Twitter summary community Howard-Rheingold network smartmobs collaboration ubiquity</dc:subject>
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<item rdf:about="http://hunch.net/?p=317">
    <title>Machine Learning (Theory) » The Meaning of Confidence</title>
    <dc:date>2008-02-20T00:44:44+00:00</dc:date>
    <link>http://hunch.net/?p=317</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>statistics advice summary research experiment explanation writing probability</dc:subject>
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    <title>Semantic analysis: Making sense of the chaos of free text « Matt’s Musings</title>
    <dc:date>2007-06-08T12:16:49+00:00</dc:date>
    <link>http://magia3e.wordpress.com/2007/05/29/semantic-analysis-making-sense-of-the-chaos-of-free-text/</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>via:tsuomela semantic analysis text mining data-mining summary machine-learning analytics web2.0</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:decbeceeb1e3/</dc:identifier>
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