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Jeffrey Pennington
Jeffrey Pennington
Google Brain
在 google.com 的电子邮件经过验证
标题
引用次数
引用次数
年份
Glove: Global vectors for word representation
J Pennington, R Socher, CD Manning
Proceedings of the 2014 conference on empirical methods in natural language …, 2014
408282014
Semi-supervised recursive autoencoders for predicting sentiment distributions
R Socher, J Pennington, EH Huang, AY Ng, CD Manning
Proceedings of the 2011 conference on empirical methods in natural language …, 2011
17332011
Deep neural networks as gaussian processes
J Lee, Y Bahri, R Novak, SS Schoenholz, J Pennington, J Sohl-Dickstein
arXiv preprint arXiv:1711.00165, 2017
11682017
Dynamic pooling and unfolding recursive autoencoders for paraphrase detection
R Socher, EH Huang, J Pennington, CD Manning, AY Ng
Advances in Neural Information Processing Systems 2011, 801--809, 2011
11582011
Wide neural networks of any depth evolve as linear models under gradient descent
J Lee, L Xiao, S Schoenholz, Y Bahri, R Novak, J Sohl-Dickstein, ...
Advances in neural information processing systems 32, 2019
10032019
Sensitivity and generalization in neural networks: an empirical study
R Novak, Y Bahri, DA Abolafia, J Pennington, J Sohl-Dickstein
arXiv preprint arXiv:1802.08760, 2018
4672018
Dynamical isometry and a mean field theory of cnns: How to train 10,000-layer vanilla convolutional neural networks
L Xiao, Y Bahri, J Sohl-Dickstein, S Schoenholz, J Pennington
International Conference on Machine Learning, 5393-5402, 2018
3482018
Bayesian deep convolutional networks with many channels are gaussian processes
R Novak, L Xiao, J Lee, Y Bahri, G Yang, J Hron, DA Abolafia, ...
arXiv preprint arXiv:1810.05148, 2018
3382018
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice
J Pennington, S Schoenholz, S Ganguli
Advances in neural information processing systems 30, 2017
2892017
Statistical mechanics of deep learning
Y Bahri, J Kadmon, J Pennington, SS Schoenholz, J Sohl-Dickstein, ...
Annual Review of Condensed Matter Physics 11, 501-528, 2020
2372020
Nonlinear random matrix theory for deep learning
J Pennington, P Worah
Advances in neural information processing systems 30, 2017
2192017
Hexagon functions and the three-loop remainder function
LJ Dixon, JM Drummond, M von Hippel, J Pennington
Journal of High Energy Physics 2013 (12), 1-95, 2013
2062013
Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)
J Pennington, R Socher, C Manning
GloVe: Global Vectors for Word Representation, 1532-1543, 2014
2052014
A mean field theory of batch normalization
G Yang, J Pennington, V Rao, J Sohl-Dickstein, SS Schoenholz
arXiv preprint arXiv:1902.08129, 2019
1892019
Finite versus infinite neural networks: an empirical study
J Lee, S Schoenholz, J Pennington, B Adlam, L Xiao, R Novak, ...
Advances in Neural Information Processing Systems 33, 15156-15172, 2020
1852020
The four-loop remainder function and multi-Regge behavior at NNLLA in planar = 4 super-Yang-Mills theory
LJ Dixon, JM Drummond, C Duhr, J Pennington
Journal of High Energy Physics 2014 (6), 1-59, 2014
1822014
The emergence of spectral universality in deep networks
J Pennington, S Schoenholz, S Ganguli
International Conference on Artificial Intelligence and Statistics, 1924-1932, 2018
1692018
Geometry of neural network loss surfaces via random matrix theory
J Pennington, Y Bahri
International conference on machine learning, 2798-2806, 2017
1572017
Single-valued harmonic polylogarithms and the multi-Regge limit
LJ Dixon, C Duhr, J Pennington
Journal of High Energy Physics 2012 (10), 1-68, 2012
1532012
Provable benefit of orthogonal initialization in optimizing deep linear networks
W Hu, L Xiao, J Pennington
arXiv preprint arXiv:2001.05992, 2020
1252020
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