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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="http://arxiv.org/abs/1505.01866"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1507.06149"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1302.2430"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1409.3059"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1311.2626"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1402.6664"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1306.0543"/>
	<rdf:li rdf:resource="http://www.nature.com/nature/journal/v481/n7381/full/nature10724.html"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1006.3913"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1005.0950"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1001.5241"/>
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  </channel><item rdf:about="http://arxiv.org/abs/1505.01866">
    <title>[1505.01866] DART: Dropouts meet Multiple Additive Regression Trees</title>
    <dc:date>2015-09-19T12:18:44+00:00</dc:date>
    <link>http://arxiv.org/abs/1505.01866</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Multiple Additive Regression Trees (MART), an ensemble model of boosted regression trees, is known to deliver high prediction accuracy for diverse tasks, and it is widely used in practice. However, it suffers an issue which we call over-specialization, wherein trees added at later iterations tend to impact the prediction of only a few instances, and make negligible contribution towards the remaining instances. This negatively affects the performance of the model on unseen data, and also makes the model over-sensitive to the contributions of the few, initially added tress. We show that the commonly used tool to address this issue, that of shrinkage, alleviates the problem only to a certain extent and the fundamental issue of over-specialization still remains. In this work, we explore a different approach to address the problem that of employing dropouts, a tool that has been recently proposed in the context of learning deep neural networks. We propose a novel way of employing dropouts in MART, resulting in the DART algorithm. We evaluate DART on ranking, regression and classification tasks, using large scale, publicly available datasets, and show that DART outperforms MART in each of the tasks, with a significant margin. We also show that DART overcomes the issue of over-specialization to a considerable extent.
]]></description>
<dc:subject>machine-learning data-analysis metaheuristics stochastic-resonance performance-measure parsimony nudge-targets Pareto-GP</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:226c5ca42030/</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:data-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaheuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:stochastic-resonance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:parsimony"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Pareto-GP"/>
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<item rdf:about="http://arxiv.org/abs/1507.06149">
    <title>[1507.06149] Data-free parameter pruning for Deep Neural Networks</title>
    <dc:date>2015-09-19T11:07:22+00:00</dc:date>
    <link>http://arxiv.org/abs/1507.06149</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Deep Neural nets (NNs) with millions of parameters are at the heart of many state-of-the-art computer vision systems today. However, recent works have shown that much smaller models can achieve similar levels of performance. In this work, we address the problem of pruning parameters in a trained NN model. Instead of removing individual weights one at a time as done in previous works, we remove one neuron at a time. We show how similar neurons are redundant, and propose a systematic way to remove them. Our experiments in pruning the densely connected layers show that we can remove upto 85\% of the total parameters in an MNIST-trained network, and about 35\% for AlexNet without significantly affecting performance. Our method can be applied on top of most networks with a fully connected layer to give a smaller network.
]]></description>
<dc:subject>deep-learning neural-networks metaheuristics parsimony jesus-christ-people party-like-it's-1995 multiobjective-optimization nudge-targets do-it-again</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a5b62c909153/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaheuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:parsimony"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:jesus-christ-people"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:party-like-it's-1995"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multiobjective-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:do-it-again"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1302.2430">
    <title>[1302.2430] On Computing the Maximum Parsimony Score of a Phylogenetic Network</title>
    <dc:date>2014-09-24T11:25:21+00:00</dc:date>
    <link>http://arxiv.org/abs/1302.2430</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Phylogenetic networks are used to display the relationship of different species whose evolution is not treelike, which is the case, for instance, in the presence of hybridization events or horizontal gene transfers. Tree inference methods such as Maximum Parsimony need to be modified in order to be applicable to networks. In this paper, we discuss two different definitions of Maximum Parsimony on networks, "hardwired" and "softwired", and examine the complexity of computing them given a network topology and a character. By exploiting a link with the problem Multicut, we show that computing the hardwired parsimony score for 2-state characters is polynomial-time solvable, while for characters with more states this problem becomes NP-hard but is still approximable and fixed parameter tractable in the parsimony score. On the other hand we show that, for the softwired definition, obtaining even weak approximation guarantees is already difficult for binary characters and restricted network topologies, and fixed-parameter tractable algorithms in the parsimony score are unlikely. On the positive side we show that computing the softwired parsimony score is fixed-parameter tractable in the level of the network, a natural parameter describing how tangled reticulate activity is in the network. Finally, we show that both the hardwired and softwired parsimony score can be computed efficiently using Integer Linear Programming. The software has been made freely available.
]]></description>
<dc:subject>cladistics parsimony algorithms networks bioinformatics operations-research computational-complexity nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c576454fc27a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cladistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:parsimony"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computational-complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1409.3059">
    <title>[1409.3059] Model selection and hypothesis testing for large-scale network models with overlapping groups</title>
    <dc:date>2014-09-22T09:58:57+00:00</dc:date>
    <link>http://arxiv.org/abs/1409.3059</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The effort to understand network systems in increasing detail has resulted in a diversity of generative models that describe large-scale structure in a variety of ways, and allow its characterization in a principled and powerful manner. Current models include features such as degree correction, where nodes with arbitrary degrees can belong to the same group, and community overlap, where nodes are allowed to belong to more than one group. However, such elaborations invariably result in an increased number of parameters, which makes these model variants prone to overfitting. Without properly accounting for the increased model complexity, one should naturally expect these larger models to "better" fit empirical networks, regardless of the actual statistical evidence supporting them. Here we propose a principled method of model selection based on the minimum description length principle and posterior odds ratios that is capable of fully accounting for the increased degrees of freedom of the larger models, and selects the best model according to the statistical evidence available in the data. Contrary to other alternatives such as likelihood ratios and parametric bootstrapping, this method scales very well, and combined with efficient inference methods recently developed, allows for the analysis of very large networks with an arbitrarily large number of groups. In applying this method to many empirical datasets from different fields, we observed that while degree correction tends to provide better fits for a majority of networks, community overlap does not, and is selected as better model only for a minority of them.
]]></description>
<dc:subject>model-selection statistics network-theory performance-measure multiobjective-optimization parsimony rather-interesting nudge-targets consider:not-doing-it-in-that-order Pareto-GP</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ecd2eb2fd7cb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:model-selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:network-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multiobjective-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:parsimony"/>
	<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:not-doing-it-in-that-order"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Pareto-GP"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1311.2626">
    <title>[1311.2626] Second-order Shape Optimization for Geometric Inverse Problems in Vision</title>
    <dc:date>2014-07-06T12:25:42+00:00</dc:date>
    <link>http://arxiv.org/abs/1311.2626</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We develop a method for optimization in shape spaces, i.e., sets of surfaces modulo re-parametrization. Unlike previously proposed gradient flows, we achieve superlinear convergence rates through a subtle approximation of the shape Hessian, which is generally hard to compute and suffers from a series of degeneracies. Our analysis highlights the role of mean curvature motion in comparison with first-order schemes: instead of surface area, our approach penalizes deformation, either by its Dirichlet energy or total variation. Latter regularizer sparks the development of an alternating direction method of multipliers on triangular meshes. Therein, a conjugate-gradients solver enables us to bypass formation of the Gaussian normal equations appearing in the course of the overall optimization. We combine all of the aforementioned ideas in a versatile geometric variation-regularized Levenberg-Marquardt-type method applicable to a variety of shape functionals, depending on intrinsic properties of the surface such as normal field and curvature as well as its embedding into space. Promising experimental results are reported.
]]></description>
<dc:subject>image-processing image-segmentation inference models modeling algorithms parsimony detail-oriented nudge-targets consider:stress-testing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:302d575e397d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-segmentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:parsimony"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:detail-oriented"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:stress-testing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1402.6664">
    <title>[1402.6664] On the role of simplicity in science</title>
    <dc:date>2014-03-01T13:59:00+00:00</dc:date>
    <link>http://arxiv.org/abs/1402.6664</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Simple assumptions represent a decisive reason to prefer one theory to another in everyday scientific praxis. But this praxis has little philosophical justification, since there exist many notions of simplicity, and those that can be defined precisely strongly depend on the language in which the theory is formulated. The language dependence is a natural feature - to some extent - but it is also believed to be a fatal problem, because, according to a common general argument, the simplicity of a theory is always trivial in a suitably chosen language. But, this trivialization argument is typically either applied to toy-models of scientific theories or applied with little regard for the empirical content of the theory. This paper shows that the trivialization argument fails, when one considers realistic theories and requires their empirical content to be preserved. In fact, the concepts that enable a very simple formulation, are not necessarily measurable, in general. Moreover, the inspection of a theory describing a chaotic billiard shows that precisely those concepts that naturally make the theory extremely simple are provably not measurable. This suggests that - whenever a theory possesses sufficiently complex consequences - the constraint of measurability prevents too simple formulations in any language. This explains why the scientists often regard their assessments of simplicity as largely unambiguous. In order to reveal a cultural bias in the scientists' assessment, one should explicitly identify different characterizations of simplicity of the assumptions that lead to different theory selections. General arguments are not sufficient.
]]></description>
<dc:subject>philosophy philosophy-of-science parsimony</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4d5b14c1e490/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:parsimony"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1306.0543">
    <title>[1306.0543] Predicting Parameters in Deep Learning</title>
    <dc:date>2013-06-07T12:40:57+00:00</dc:date>
    <link>http://arxiv.org/abs/1306.0543</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We demonstrate that there is significant redundancy in the parameterization of several deep learning models. Given only a few weight values for each feature it is possible to accurately predict the remaining values. Moreover, we show that not only can the parameter values be predicted, but many of them need not be learned at all. We train several different architectures by learning only a small number of weights and predicting the rest. In the best case we are able to predict more than 95% of the weights of a network without any drop in accuracy.
]]></description>
<dc:subject>neural-networks deep-learning efficiency-is-on-the-table-now-I-see parsimony performance-measure models-and-modes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9fb255782539/</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:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:efficiency-is-on-the-table-now-I-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:parsimony"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nature.com/nature/journal/v481/n7381/full/nature10724.html">
    <title>Evolution of increased complexity in a molecular machine : Nature : Nature Publishing Group</title>
    <dc:date>2012-01-21T11:53:45+00:00</dc:date>
    <link>http://www.nature.com/nature/journal/v481/n7381/full/nature10724.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Many cellular processes are carried out by molecular ‘machines’—assemblies of multiple differentiated proteins that physically interact to execute biological functions1, 2, 3, 4, 5, 6, 7, 8. Despite much speculation, strong evidence of the mechanisms by which these assemblies evolved is lacking. Here we use ancestral gene resurrection9, 10, 11 and manipulative genetic experiments to determine how the complexity of an essential molecular machine—the hexameric transmembrane ring of the eukaryotic V-ATPase proton pump—increased hundreds of millions of years ago. We show that the ring of Fungi, which is composed of three paralogous proteins, evolved from a more ancient two-paralogue complex because of a gene duplication that was followed by loss in each daughter copy of specific interfaces by which it interacts with other ring proteins. These losses were complementary, so both copies became obligate components with restricted spatial roles in the complex. Reintroducing a single historical mutation from each paralogue lineage into the resurrected ancestral proteins is sufficient to recapitulate their asymmetric degeneration and trigger the requirement for the more elaborate three-component ring. Our experiments show that increased complexity in an essential molecular machine evolved because of simple, high-probability evolutionary processes, without the apparent evolution of novel functions. They point to a plausible mechanism for the evolution of complexity in other multi-paralogue protein complexes."]]></description>
<dc:subject>via:cshalizi evolution structural-biology parsimony dangers-of-premature-optimization lesson-for-genetic-programming</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4916779c8b1b/</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:evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:parsimony"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dangers-of-premature-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:lesson-for-genetic-programming"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1006.3913">
    <title>[1006.3913] A Method for Accelerating Conway's Doomsday Algorithm</title>
    <dc:date>2010-06-29T00:18:26+00:00</dc:date>
    <link>http://arxiv.org/abs/1006.3913</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Also: how about an inverse Doomsday algorithm. "We propose a simplification of a key component in the Doomsday Algorithm for calculating the day-of-the-week of any given date.…"
]]></description>
<dc:subject>nudge-targets algorithms John-Horton-Conway numerical-methods parsimony</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c2f43bbcabfd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:John-Horton-Conway"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:numerical-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:parsimony"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1005.0950">
    <title>[1005.0950] On Duplication in Mathematical Repositories</title>
    <dc:date>2010-05-09T13:41:36+00:00</dc:date>
    <link>http://arxiv.org/abs/1005.0950</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Building a repository of proof-checked mathematical knowledge is without any doubt a lot of work, and besides the actual formalization process there also is the task of maintaining the repository. Thus it seems obvious to keep a repsoitory as small as possible, in particular each piece of mathematical knowledge should be formalized only once. In this paper, however, we claim that it might be reasonable or even necessary to duplicate knowledge in a mathematical repository. We analyze different situations and reasons for doing so and provide a number of examples supporting our thesis."
]]></description>
<dc:subject>parsimony pragmatism library2.0 mathematics linguistics that-Gödel-fellow-said-something-relevant</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:36f3e3e44d07/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:parsimony"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pragmatism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:library2.0"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:linguistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:that-Gödel-fellow-said-something-relevant"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1001.5241">
    <title>[1001.5241] Polyhedral geometry of Phylogenetic Rogue Taxa</title>
    <dc:date>2010-04-01T11:34:25+00:00</dc:date>
    <link>http://arxiv.org/abs/1001.5241</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Find myself wondering if one could develop an algorithm to "invent" a taxon that would maximally disrupt the resulting tree structure if added…
]]></description>
<dc:subject>nudge-targets phylogenetics taxonomy models-and-modes parsimony Occam's-double-edged-razor</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c72f47bf0ad3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:phylogenetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:taxonomy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:parsimony"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Occam's-double-edged-razor"/>
</rdf:Bag></taxo:topics>
</item>
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