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We can define a neural network that can learn to recognize objects in less than 100 lines of code.
Non-vacuous generalization bounds at the imagenet scale: a pac-bayesian compression approach
Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P Adams, and Peter Orbanz · 1907
Earlier work this paper cites.
Operant conditioning of cortical unit activity
Eberhard E Fetz · 1969
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Adaptive gain control of vestibuloocular reflex by the cerebellum
DA Robinson · 1976
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Recognition-by-components: a theory of human image understanding
Irving Biederman · 1987
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Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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A functional microcircuit for cat visual cortex
Rodney J Douglas and KA Martin · 1991
Earlier work this paper cites.
Learning long-term dependencies with gradient descent is difficult
Yoshua Bengio, Patrice Simard, Paolo Frasconi, et al · 1994
Earlier work this paper cites.
Principles of neural science
Eric R Kandel, James H Schwartz, Thomas M Jessell, Department of Biochemistry, Molecular Biophysics Thomas Jessell, Steven Siegelbaum, and AJ Hudspeth · 2000
Earlier work this paper cites.
Solving the mystery of insect flight
Michael Dickinson · 2001
Earlier work this paper cites.
Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks
Julian D Olden and Donald A Jackson · 2002
Earlier work this paper cites.
Distilling free-form natural laws from experimental data
Michael Schmidt and Hod Lipson · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Complex network measures of brain connectivity: uses and interpretations
Mikail Rubinov and Olaf Sporns · 2010
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Binarized neural networks on the imagenet classification task
Xundong Wu, Yong Wu, and Yong Zhao · 2016
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Synthesizing robust adversarial examples
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok · 2017
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Distilling a neural network into a soft decision tree
Nicholas Frosst and Geoffrey Hinton · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Could a neuroscientist understand a microprocessor?
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Song Han, Huizi Mao, and William J Dally · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Understanding neural networks through deep visualization
Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson · 2015
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Image style transfer using convolutional neural networks
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Eric Jonas and Konrad Paul Kording · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Measuring the intrinsic dimension of objective landscapes
Chunyuan Li, Heerad Farkhoor, Rosanne Liu, and Jason Yosinski · 2018
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Dynamic compression and expansion in a classifying recurrent network
Matthew S Farrell, Stefano Recanatesi, Guillaume Lajoie, and Eric Shea-Brown · 2019
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Humans store about 1.5 megabytes of information during language acquisition
Francis Mollica and Steven T Piantadosi · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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An algorithmic barrier to neural circuit understanding
Venkatakrishnan Ramaswamy · 2019
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