Jeffrey Pennington
Jeffrey Pennington
Google Brain
Verified email at google.com
Title
Cited by
Cited by
Year
Glove: Global vectors for word representation
J Pennington, R Socher, CD Manning
Proceedings of the 2014 conference on empirical methods in natural language …, 2014
216042014
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
14622011
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
9642011
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
4322017
Wide neural networks of any depth evolve as linear models under gradient descent
J Lee, L Xiao, SS Schoenholz, Y Bahri, R Novak, J Sohl-Dickstein, ...
arXiv preprint arXiv:1902.06720, 2019
3182019
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
2262018
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
1592018
Hexagon functions and the three-loop remainder function
LJ Dixon, JM Drummond, M von Hippel, J Pennington
Journal of High Energy Physics 2013 (12), 49, 2013
1512013
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice
J Pennington, SS Schoenholz, S Ganguli
arXiv preprint arXiv:1711.04735, 2017
1482017
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
1322018
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), 116, 2014
1312014
Single-valued harmonic polylogarithms and the multi-Regge limit
LJ Dixon, C Duhr, J Pennington
Journal of High Energy Physics 2012 (10), 74, 2012
1112012
A mean field theory of batch normalization
G Yang, J Pennington, V Rao, J Sohl-Dickstein, SS Schoenholz
arXiv preprint arXiv:1902.08129, 2019
972019
Leading singularities and off-shell conformal integrals
J Drummond, C Duhr, B Eden, P Heslop, J Pennington, VA Smirnov
Journal of High Energy Physics 2013 (8), 133, 2013
972013
Geometry of neural network loss surfaces via random matrix theory
J Pennington, Y Bahri
International Conference on Machine Learning, 2798-2806, 2017
932017
The emergence of spectral universality in deep networks
J Pennington, S Schoenholz, S Ganguli
International Conference on Artificial Intelligence and Statistics, 1924-1932, 2018
922018
Nonlinear random matrix theory for deep learning
J Pennington, P Worah
912017
Dynamical isometry and a mean field theory of RNNs: Gating enables signal propagation in recurrent neural networks
M Chen, J Pennington, S Schoenholz
International Conference on Machine Learning, 873-882, 2018
752018
Spherical random features for polynomial kernels
J Pennington, XY Felix, S Kumar
602015
Bootstrapping six-gluon scattering in planar super-Yang-Mills theory
LJ Dixon, JM Drummond, C Duhr, M von Hippel, J Pennington
arXiv preprint arXiv:1407.4724, 2014
552014
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