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    <title>Pinboard (Vaguery)</title>
    <link>https://pinboard.in/u:Vaguery/public/</link>
    <description>recent bookmarks from Vaguery</description>
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      <rdf:Seq>	<rdf:li rdf:resource="https://en.wikipedia.org/wiki/Erasure_code"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1211.3589"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1303.4375"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1108.4135"/>
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  </channel><item rdf:about="https://en.wikipedia.org/wiki/Erasure_code">
    <title>Erasure code - Wikipedia, the free encyclopedia</title>
    <dc:date>2016-07-25T12:22:52+00:00</dc:date>
    <link>https://en.wikipedia.org/wiki/Erasure_code</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>information-theory encoding rather-interesting correction nudge-targets algorithms consider:looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:802587da1571/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:encoding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:correction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
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<item rdf:about="http://arxiv.org/abs/1211.3589">
    <title>[1211.3589] A Truncated EM Approach for Spike-and-Slab Sparse Coding</title>
    <dc:date>2014-09-06T13:12:58+00:00</dc:date>
    <link>http://arxiv.org/abs/1211.3589</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We study inference and learning based on a sparse coding model with `spike-and-slab' prior. As in standard sparse coding, the model used assumes independent latent sources that linearly combine to generate data points. However, instead of using a standard sparse prior such as a Laplace distribution, we study the application of a more flexible `spike-and-slab' distribution which models the absence or presence of a source's contribution independently of its strength if it contributes. We investigate two approaches to optimize the parameters of spike-and-slab sparse coding: a novel truncated EM approach and, for comparison, an approach based on standard factored variational distributions. The truncated approach can be regarded as a variational approach with truncated posteriors as variational distributions. In applications to source separation we find that both approaches improve the state-of-the-art in a number of standard benchmarks, which argues for the use of `spike-and-slab' priors for the corresponding data domains. Furthermore, we find that the truncated EM approach improves on the standard factored approach in source separation tasks−which hints to biases introduced by assuming posterior independence in the factored variational approach. Likewise, on a standard benchmark for image denoising, we find that the truncated EM approach improves on the factored variational approach. While the performance of the factored approach saturates with increasing numbers of hidden dimensions, the performance of the truncated approach improves the state-of-the-art for higher noise levels.
]]></description>
<dc:subject>compression algorithms encoding nudge-targets consider:multiobjective-performance consider:multiscale-representations</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:62c3173baa26/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:compression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:encoding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:multiobjective-performance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:multiscale-representations"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1303.4375">
    <title>[1303.4375] On the Computing of the Minimum Distance of Linear Block Codes by Heuristic Methods</title>
    <dc:date>2013-04-08T19:13:51+00:00</dc:date>
    <link>http://arxiv.org/abs/1303.4375</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The evaluation of the minimum distance of linear block codes remains an open problem in coding theory, and it is not easy to determine its true value by classical methods, for this reason the problem has been solved in the literature with heuristic techniques such as genetic algorithms and local search algorithms. In this paper we propose two approaches to attack the hardness of this problem. The first approach is based on genetic algorithms and it yield to good results comparing to another work based also on genetic algorithms. The second approach is based on a new randomized algorithm which we call Multiple Impulse Method MIM, where the principle is to search codewords locally around the all-zero codeword perturbed by a minimum level of noise, anticipating that the resultant nearest nonzero codewords will most likely contain the minimum Hamming-weight codeword whose Hamming weight is equal to the minimum distance of the linear code.]]></description>
<dc:subject>representation evolutionary-algorithms search-algorithms nudge-targets performance-measure encoding</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:662dc04b1192/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:search-algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:encoding"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1108.4135">
    <title>[1108.4135] Complex-Valued Autoencoders</title>
    <dc:date>2011-12-18T12:44:02+00:00</dc:date>
    <link>http://arxiv.org/abs/1108.4135</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Autoencoders are unsupervised machine learning circuits whose learning goal is to minimize a distortion measure between inputs and outputs. Linear autoencoders can be defined over any field and only real-valued linear autoencoder have been studied so far. Here we study complex-valued linear autoencoders where the components of the training vectors and adjustable matrices are defined over the complex field with the $L_2$ norm. We provide simpler and more general proofs that unify the real-valued and complex-valued cases, showing that in both cases the landscape of the error function is invariant under certain groups of transformations. The landscape has no local minima, a family of global minima associated with Principal Component Analysis, and many families of saddle points associated with orthogonal projections onto sub-space spanned by sub-optimal subsets of eigenvectors of the covariance matrix. The theory yields several iterative, convergent, learning algorithms, a clear understanding of the generalization properties of the trained autoencoders, and can equally be applied to the hetero-associative case when external targets are provided. Partial results on deep architecture as well as the differential geometry of autoencoders are also presented. The general framework described here is useful to classify autoencoders and identify general common properties that ought to be investigated for each class, illuminating some of the connections between information theory, unsupervised learning, clustering, Hebbian learning, and auto encoders."]]></description>
<dc:subject>neural-networks machine-learning classification encoding algorithms nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:cdffef14599f/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:classification"/>
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