deploy: e31c06f

_sources/stubs/2014_PankajMehta_AnExactMappingBetweenTheVariat.md

@@ -0,0 +1,15 @@
+---
+jupytext:
+  formats: md:myst
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  language: python
+  name: python3
+---
+
+# "An exact mapping between the Variational Renormalization Group and Deep Learning"
+
+https://arxiv.org/abs/1410.3831

_sources/stubs/2015_ArnabPaul_WhyDoesDeepLearningWorkAPerspe.md

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+---
+jupytext:
+  formats: md:myst
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  language: python
+  name: python3
+---
+
+# "Why does Deep Learning work? - A perspective from Group Theory"
+
+https://arxiv.org/abs/1412.6621

content.html

@@ -261,10 +261,12 @@
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_KarenSimonyan_VeryDeepConvolutionalNetworksF.html">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_NitishSrivastava_DropoutASimpleWayToPreventNeur.html">“Dropout: A Simple Way to Prevent Neural Networks from Overfitting”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_OmerLevy_NeuralWordEmbeddingAsImplicitM.html">“Neural Word Embedding as Implicit Matrix Factorization”</a></li>
+<li class="toctree-l2"><a class="reference internal" href="stubs/2014_PankajMehta_AnExactMappingBetweenTheVariat.html">“An exact mapping between the Variational Renormalization Group and Deep Learning”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_YannDauphin_IdentifyingAndAttackingTheSadd.html">“Identifying and attacking the saddle point problem in high-dimensional non-convex optimization”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_YiHsuanTsai_LocalityPreservingHashing.html">“LOCALITY PRESERVING HASHING”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014__OnThePropertiesOfNeuralMachine.html">“On the Properties of Neural Machine Translation: Encoder–Decoder Approaches”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_AlexanderMordvintsev_InceptionismGoingDeeperIntoNeu.html">“Inceptionism: Going Deeper into Neural Networks”</a></li>
+<li class="toctree-l2"><a class="reference internal" href="stubs/2015_ArnabPaul_WhyDoesDeepLearningWorkAPerspe.html">“Why does Deep Learning work? - A perspective from Group Theory”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_DSculley_HiddenTechnicalDebtInMachineLe.html">“Hidden Technical Debt in Machine Learning Systems”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_GeoffreyHinton_DistillingTheKnowledgeInANeura.html">“Distilling the Knowledge in a Neural Network”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_GregoryKoch_SiameseNeuralNetworksForOneSho.html">“Siamese Neural Networks for One-Shot Image Recognition”</a></li>

genindex.html

@@ -258,10 +258,12 @@
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_KarenSimonyan_VeryDeepConvolutionalNetworksF.html">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_NitishSrivastava_DropoutASimpleWayToPreventNeur.html">“Dropout: A Simple Way to Prevent Neural Networks from Overfitting”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_OmerLevy_NeuralWordEmbeddingAsImplicitM.html">“Neural Word Embedding as Implicit Matrix Factorization”</a></li>
+<li class="toctree-l2"><a class="reference internal" href="stubs/2014_PankajMehta_AnExactMappingBetweenTheVariat.html">“An exact mapping between the Variational Renormalization Group and Deep Learning”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_YannDauphin_IdentifyingAndAttackingTheSadd.html">“Identifying and attacking the saddle point problem in high-dimensional non-convex optimization”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_YiHsuanTsai_LocalityPreservingHashing.html">“LOCALITY PRESERVING HASHING”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014__OnThePropertiesOfNeuralMachine.html">“On the Properties of Neural Machine Translation: Encoder–Decoder Approaches”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_AlexanderMordvintsev_InceptionismGoingDeeperIntoNeu.html">“Inceptionism: Going Deeper into Neural Networks”</a></li>
+<li class="toctree-l2"><a class="reference internal" href="stubs/2015_ArnabPaul_WhyDoesDeepLearningWorkAPerspe.html">“Why does Deep Learning work? - A perspective from Group Theory”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_DSculley_HiddenTechnicalDebtInMachineLe.html">“Hidden Technical Debt in Machine Learning Systems”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_GeoffreyHinton_DistillingTheKnowledgeInANeura.html">“Distilling the Knowledge in a Neural Network”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_GregoryKoch_SiameseNeuralNetworksForOneSho.html">“Siamese Neural Networks for One-Shot Image Recognition”</a></li>

intro.html

@@ -260,10 +260,12 @@
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_KarenSimonyan_VeryDeepConvolutionalNetworksF.html">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_NitishSrivastava_DropoutASimpleWayToPreventNeur.html">“Dropout: A Simple Way to Prevent Neural Networks from Overfitting”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_OmerLevy_NeuralWordEmbeddingAsImplicitM.html">“Neural Word Embedding as Implicit Matrix Factorization”</a></li>
+<li class="toctree-l2"><a class="reference internal" href="stubs/2014_PankajMehta_AnExactMappingBetweenTheVariat.html">“An exact mapping between the Variational Renormalization Group and Deep Learning”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_YannDauphin_IdentifyingAndAttackingTheSadd.html">“Identifying and attacking the saddle point problem in high-dimensional non-convex optimization”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_YiHsuanTsai_LocalityPreservingHashing.html">“LOCALITY PRESERVING HASHING”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014__OnThePropertiesOfNeuralMachine.html">“On the Properties of Neural Machine Translation: Encoder–Decoder Approaches”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_AlexanderMordvintsev_InceptionismGoingDeeperIntoNeu.html">“Inceptionism: Going Deeper into Neural Networks”</a></li>
+<li class="toctree-l2"><a class="reference internal" href="stubs/2015_ArnabPaul_WhyDoesDeepLearningWorkAPerspe.html">“Why does Deep Learning work? - A perspective from Group Theory”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_DSculley_HiddenTechnicalDebtInMachineLe.html">“Hidden Technical Debt in Machine Learning Systems”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_GeoffreyHinton_DistillingTheKnowledgeInANeura.html">“Distilling the Knowledge in a Neural Network”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_GregoryKoch_SiameseNeuralNetworksForOneSho.html">“Siamese Neural Networks for One-Shot Image Recognition”</a></li>

markdown.html

@@ -261,10 +261,12 @@
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_KarenSimonyan_VeryDeepConvolutionalNetworksF.html">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_NitishSrivastava_DropoutASimpleWayToPreventNeur.html">“Dropout: A Simple Way to Prevent Neural Networks from Overfitting”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_OmerLevy_NeuralWordEmbeddingAsImplicitM.html">“Neural Word Embedding as Implicit Matrix Factorization”</a></li>
+<li class="toctree-l2"><a class="reference internal" href="stubs/2014_PankajMehta_AnExactMappingBetweenTheVariat.html">“An exact mapping between the Variational Renormalization Group and Deep Learning”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_YannDauphin_IdentifyingAndAttackingTheSadd.html">“Identifying and attacking the saddle point problem in high-dimensional non-convex optimization”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_YiHsuanTsai_LocalityPreservingHashing.html">“LOCALITY PRESERVING HASHING”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014__OnThePropertiesOfNeuralMachine.html">“On the Properties of Neural Machine Translation: Encoder–Decoder Approaches”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_AlexanderMordvintsev_InceptionismGoingDeeperIntoNeu.html">“Inceptionism: Going Deeper into Neural Networks”</a></li>
+<li class="toctree-l2"><a class="reference internal" href="stubs/2015_ArnabPaul_WhyDoesDeepLearningWorkAPerspe.html">“Why does Deep Learning work? - A perspective from Group Theory”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_DSculley_HiddenTechnicalDebtInMachineLe.html">“Hidden Technical Debt in Machine Learning Systems”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_GeoffreyHinton_DistillingTheKnowledgeInANeura.html">“Distilling the Knowledge in a Neural Network”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_GregoryKoch_SiameseNeuralNetworksForOneSho.html">“Siamese Neural Networks for One-Shot Image Recognition”</a></li>

notebooks.html

@@ -263,10 +263,12 @@
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_KarenSimonyan_VeryDeepConvolutionalNetworksF.html">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_NitishSrivastava_DropoutASimpleWayToPreventNeur.html">“Dropout: A Simple Way to Prevent Neural Networks from Overfitting”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_OmerLevy_NeuralWordEmbeddingAsImplicitM.html">“Neural Word Embedding as Implicit Matrix Factorization”</a></li>
+<li class="toctree-l2"><a class="reference internal" href="stubs/2014_PankajMehta_AnExactMappingBetweenTheVariat.html">“An exact mapping between the Variational Renormalization Group and Deep Learning”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_YannDauphin_IdentifyingAndAttackingTheSadd.html">“Identifying and attacking the saddle point problem in high-dimensional non-convex optimization”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014_YiHsuanTsai_LocalityPreservingHashing.html">“LOCALITY PRESERVING HASHING”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2014__OnThePropertiesOfNeuralMachine.html">“On the Properties of Neural Machine Translation: Encoder–Decoder Approaches”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_AlexanderMordvintsev_InceptionismGoingDeeperIntoNeu.html">“Inceptionism: Going Deeper into Neural Networks”</a></li>
+<li class="toctree-l2"><a class="reference internal" href="stubs/2015_ArnabPaul_WhyDoesDeepLearningWorkAPerspe.html">“Why does Deep Learning work? - A perspective from Group Theory”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_DSculley_HiddenTechnicalDebtInMachineLe.html">“Hidden Technical Debt in Machine Learning Systems”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_GeoffreyHinton_DistillingTheKnowledgeInANeura.html">“Distilling the Knowledge in a Neural Network”</a></li>
 <li class="toctree-l2"><a class="reference internal" href="stubs/2015_GregoryKoch_SiameseNeuralNetworksForOneSho.html">“Siamese Neural Networks for One-Shot Image Recognition”</a></li>
@@ -620,7 +622,7 @@ <h2>Code blocks and outputs<a class="headerlink" href="#code-blocks-and-outputs"
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;contextlib.ExitStack at 0x7f1aa8fc1b80&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;contextlib.ExitStack at 0x7fd604bc28e0&gt;
 </pre></div>
 </div>
 </div>
@@ -649,7 +651,7 @@ <h2>Code blocks and outputs<a class="headerlink" href="#code-blocks-and-outputs"
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="_images/b0a1b4361f3a10cd5f5e7b58f3f9744c85db1e2ea029a273d3a4b61e5d64741d.png" src="_images/b0a1b4361f3a10cd5f5e7b58f3f9744c85db1e2ea029a273d3a4b61e5d64741d.png" />
+<img alt="_images/6d74b9abc36bf12bacb2941621b662848e6565366f81c1c0324aaa62acb5b276.png" src="_images/6d74b9abc36bf12bacb2941621b662848e6565366f81c1c0324aaa62acb5b276.png" />
 </div>
 </div>
 <p>There is a lot more that you can do with outputs (such as including interactive outputs)