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Deep Neural Networks can generalize despite being significantly overparametrized.
The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network
Peter L Bartlett · 1998
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(not) bounding the true error
John Langford and Rich Caruana · 2001
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Untangling invariant object recognition
James J DiCarlo and David D Cox · 2007
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Object features used by humans and monkeys to identify rotated shapes
Kristina J Nielsen, Nikos K Logothetis, and Gregor Rainer · 2008
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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In search of the real inductive bias: On the role of implicit regularization in deep learning
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 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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Norm-based capacity control in neural networks
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2015
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Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence
Radoslaw Martin Cichy, Aditya Khosla, Dimitrios Pantazis, Antonio Torralba, and Aude Oliva · 2016
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On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2016
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Learning deep parsimonious representations
Renjie Liao, Alex Schwing, Richard Zemel, and Raquel Urtasun · 2016
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
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Gintare Karolina Dziugaite and Daniel M Roy · 2017
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Comparing deep neural networks against humans: object recognition when the signal gets weaker
Large margin deep networks for classification
Gamaleldin Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, and Samy Bengio · 2018
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Generalisation in humans and deep neural networks
Robert Geirhos, Carlos RM Temme, Jonas Rauber, Heiko H Schütt, Matthias Bethge, and Felix A Wichmann · 2018
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Predicting the generalization gap in deep networks with margin distributions
Yiding Jiang, Dilip Krishnan, Hossein Mobahi, and Samy Bengio · 2018
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On the importance of single directions for generalization
Ari S Morcos, David GT Barrett, Neil C Rabinowitz, and Matthew Botvinick · 2018
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Clustering convolutional kernels to compress deep neural networks
Sanghyun Son, Seungjun Nah, and Kyoung Mu Lee · 2018
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Robert Geirhos, David HJ Janssen, Heiko H Schütt, Jonas Rauber, Matthias Bethge, and Felix A Wichmann · 2017
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Invariant object recognition is a personalized selection of invariant features in humans, not simply explained by hierarchical feed-forward vision models
Hamid Karimi-Rouzbahani, Nasour Bagheri, and Reza Ebrahimpour · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Emergence of invariance and disentanglement in deep representations
Alessandro Achille and Stefano Soatto · 2018
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Stronger generalization bounds for deep nets via a compression approach
Sanjeev Arora, Rong Ge, Behnam Neyshabur, and Yi Zhang · 2018
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Fantastic generalization measures and where to find them
Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, and Samy Bengio · 2019
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Manifold mixup: Better representations by interpolating hidden states
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, and Yoshua Bengio · 2019
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Separability and geometry of object manifolds in deep neural networks
Uri Cohen, SueYeon Chung, Daniel D Lee, and Haim Sompolinsky · 2020
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