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It has been hypothesized that some form of "modular" structure in artificial neural networks should be useful for learning, compositionality, and generalization.
Similarity of Neural Network Representations Revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton · 1905
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The Architecture of Complexity
Herbert A Simon · 1962
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Large Automatic Learning, Rule Extraction, and Generalization
J Denker, D Schwartz, B Wittner, S Solla, R Howard, L Jackel, and J Hopfield · 1987
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Why are ”what” and ”where” processed by separate cortical visual systems? A computational investigation
J. G. Rueckl, K. R. Cave, and S. M. Kosslyn · 1989
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Task Decomposition Through Competition in a Modular Connectionist Architecture:The What and Where Vision Tasks
Robert A Jacobs, Michael I Jordan, and Andrew G Barto · 1991
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Separate visual pathways for perception and action
Melvyn A. Goodale and A. David Milner · 1992
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Gradient-Based Learning Applied to Document Recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Biologically inspired modular neural networks
Farooq Azam · 2000
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Evolving Modular Architectures for Neural Networks
Andrea Di Ferdinando, Raffaele Calabretta, and Domenico Parisi · 2001
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Topographic independent component analysis
Aapo Hyvärinen, Patrik O. Hoyer, and Mika Inki · 2001
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Community structure in social and biological networks
M. Girvan and M. E.J. Newman · 2002
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Kernel independent component analysis
Francis R. Bach and Michael I. Jordan · 2003
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Beyond independent components: Trees and clusters
Francis R. Bach and Michael I. Jordan · 2003
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Finding and evaluating community structure in networks
M. E.J. Newman and M. Girvan · 2004
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Measuring statistical dependence with Hilbert-Schmidt norms
Arthur Gretton, Olivier Bousquet, Alex Smola, and Bernhard Schölkopf · 2005
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Spontaneous evolution of modularity and network motifs
Nadav Kashtan and Uri Alon · 2005
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Independent Subspace Analysis Using Geodesic Spanning Trees
Barnabás Póczos and András Lõrincz · 2005
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Modularity and community structure in networks
M. E.J. Newman · 2006
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On Modularity Clustering
U. Brandes, D. Delling, M. Gaertler, R. Gorke, M. Hoefer, Z. Nikoloski, and D. Wagner · 2007
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Independent Subspace Analysis is Unique, Given Irreducibility
Harold W Gutch and Fabian J Theis · 2007
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Varying environments can speed up evolution
Nadav Kashtan, Elad Noor, and Uri Alon · 2007
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Principles of modularity, regularity, and hierarchy for scalable systems
H Lipson · 2007
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The road to modularity
Günter P. Wagner, Mihaela Pavlicev, and James M. Cheverud · 2007
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Learning deep disentangled embeddings with the F-statistic loss
Karl Ridgeway and Michael C. Mozer · 2018
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A review of modularization techniques in artificial neural networks
Mohammed Amer and Tomás Maul · 2019
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Measuring compositionality in representation learning
Jacob Andreas · 2019
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Teleosemantics, Selection and Novel Contents
Justin Garson and David Papineau · 2019
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Element-centric clustering comparison unifies overlaps and hierarchy
Alexander J. Gates, Ian B. Wood, William P. Hetrick, and Yong Yeol Ahn · 2019
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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2019
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Róbert Csordás, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2010
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Gnu parallel - the command-line power tool
O. Tange · 2011
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The evolutionary origins of modularity
Jeff Clune, Jean Baptiste Mouret, and Hod Lipson · 2012
Cited alongside, same era.
Algorithms for learning kernels based on centered alignment
Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh · 2012
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Contrast functions for independent subspace analysis
Jason A. Palmer and Scott Makeig · 2012
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 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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Knowledge discovery from layered neural networks based on non-negative task matrix decomposition
Chihiro Watanabe, Kaoru Hiramatsu, and Kunio Kashino · 2020
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The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning
Shahab Bakhtiari, Patrick Mineault, Tim Lillicrap Deepmind, Christopher C Pack, and Blake A Richards · 2021
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Extreme sparsity gives rise to functional specialization
Gabriel Béna and Dan F. M. Goodman · 2021
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Clusterability in Neural Networks
Daniel Filan, Stephen Casper, Shlomi Hod, Cody Wild, Andrew Critch, and Stuart Russell · 2021
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The role of disentanglement in generalization
Milton Llera Montero, Casimir JJ Ludwig, Rui Ponte Costa, Guarav Malhotra, and Jeffrey Bowers · 2021
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Toward Causal Representation Learning
Bernhard Scholkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio · 2021
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Subspace clustering via stacked independent subspace analysis networks with sparse prior information
Zongze Wu, Chunchen Su, Ming Yin, Zhigang Ren, and Shengli Xie · 2021
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