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We analyze architectural features of Deep Neural Networks (DNNs) using the so-called Neural Tangent Kernel (NTK), which describes the training and generalization of DNNs in the infinite-width setting.
Bayesian Learning for Neural Networks
Radford M. Neal · 1996
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Kernel Methods for Deep Learning
Youngmin Cho and Lawrence K. Saul · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Efficient backprop
Yann A LeCun, Léon Bottou, Genevieve B Orr, and Klaus-Robert Müller · 2012
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Generative Adversarial Networks
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Normalization propagation: A parametric technique for removing internal covariate shift in deep networks
Devansh Arpit, Yingbo Zhou, Bhargava Kota, and Venu Govindaraju · 2016
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Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity
Amit Daniely, Roy Frostig, and Yoram Singer · 2016
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Layer Normalization
J. Lei Ba, J. R. Kiros, and G. E. Hinton · 2016
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Deconvolution and checkerboard artifacts
Augustus Odena, Vincent Dumoulin, and Chris Olah · 2016
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Exponential expressivity in deep neural networks through transient chaos
Ben Poole, Subhaneil Lahiri, Maithra Raghu, Jascha Sohl-Dickstein, and Surya Ganguli · 2016
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Durk P Kingma · 2016
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Self-normalizing neural networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter · 2017
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On the effects of batch and weight normalization in generative adversarial networks
Sitao Xiang and Hao Li · 2017
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Mean field residual networks: On the edge of chaos
A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2018
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Deep Neural Networks as Gaussian Processes
Jae Hoon Lee, Yasaman Bahri, Roman Novak, Samuel S. Schoenholz, Jeffrey Pennington, and Jascha Sohl-Dickstein · 2018
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Optimal SGD Hyperparameters for Fully Connected Networks
Daniel S Park, Samuel L Smith, Jascha Sohl-dickstein, and Quoc V Le · 2018
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How does batch normalization help optimization?
Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, and Aleksander Madry · 2018
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Self-attention generative adversarial networks
Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena · 2018
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Greg Yang and Samuel Schoenholz · 2017
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A Convergence Theory for Deep Learning via Over-Parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2018
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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
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Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach
Ryo Karakida, Shotaro Akaho, and Shun-Ichi Amari · 2018
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Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, and Ruosong Wang · 2019
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Gradient Descent Provably Optimizes Over-parameterized Neural Networks
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A mean field theory of batch normalization
Greg Yang, Jeffrey Pennington, Vinay Rao, Jascha Sohl-Dickstein, and Samuel S. Schoenholz · 2019
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