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The learned weights of a neural network are often considered devoid of scrutable internal structure.
Difference equations, isoperimetric inequality and transience of certain random walks
Jozef Dodziuk · 1984
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λ 1 \lambda_{1} , isoperimetric inequalities for graphs, and superconcentrators
Noga Alon and Vitali D Milman · 1985
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Gradient-based learning applied to document recognition
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Design rules: The power of modularity , volume 1
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Bernard V North, David Curtis, and Pak C Sham · 2002
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Alfio Borzì and Giuseppe Borzì · 2006
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Mark EJ Newman · 2006
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Grady Booch, Robert A Maksimchuk, Michael W Engle, Bobbi Young, Jim Conallen, and Kelli A Houston · 2007
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Ulrike von Luxburg · 2007
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The road to modularity
Günter P Wagner, Mihaela Pavlicev, and James M Cheverud · 2007
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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The evolutionary origins of modularity
Jeff Clune, Jean-Baptiste Mouret, and Hod Lipson · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Multiway spectral partitioning and higher-order Cheeger inequalities
James R Lee, Shayan Oveis Gharan, and Luca Trevisan · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Modular representation of layered neural networks
Chihiro Watanabe, Kaoru Hiramatsu, and Kunio Kashino · 2018
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Shan Carter, Zan Armstrong, Ludwig Schubert, Ian Johnson, and Chris Olah · 2019
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NIF: A framework for quantifying neural information flow in deep networks
Brian Davis, Umang Bhatt, Kartikeya Bhardwaj, Radu Marculescu, and José Moura · 2019
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
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Linear mode connectivity and the lottery ticket hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M Roy, and Michael Carbin · 2019
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Math 401, Graph Laplacian, 2015
Wojciech Czaja · 2015
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Learning both weights and connections for efficient neural networks
Song Han, Jeff Pool, John Tran, and William Dally · 2015
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Network science
Albert-László Barabási et al · 2016
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Feature visualization
Chris Olah, Alexander Mordvintsev, and Ludwig Schubert · 2017
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Checking functional modularity in DNN by biclustering task-specific hidden neurons
Jialin Lu and Martin Ester · 2019
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Interpretable machine learning
Christoph Molnar · 2019
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What’s hidden in a randomly weighted neural network?
Vivek Ramanujan, Mitchell Wortsman, Aniruddha Kembhavi, Ali Farhadi, and Mohammad Rastegari · 2019
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Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, and Klaus-Robert Müller, editors · 2019
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Interpreting layered neural networks via hierarchical modular representation
Chihiro Watanabe · 2019
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Understanding community structure in layered neural networks
Chihiro Watanabe, Kaoru Hiramatsu, and Kunio Kashino · 2019
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Deconstructing lottery tickets: Zeros, signs, and the supermask
Hattie Zhou, Janice Lan, Rosanne Liu, and Jason Yosinski · 2019
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Thread: Circuits
Nick Cammarata, Shan Carter, Gabriel Goh, Chris Olah, Michael Petrov, and Ludwig Schubert · 2020
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modular, January 2020
Wiktionary · 2020
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