Glove: Global vectors for word representation J Pennington, R Socher, CD Manning
Proceedings of the 2014 conference on empirical methods in natural language …, 2014
18962 2014 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
1397 2011 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
936 2011 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
370 2017 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, 8572-8583, 2019
250 2019 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
197 2018 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
145 2013 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, SS Schoenholz, J Pennington
arXiv preprint arXiv:1806.05393, 2018
140 2018 Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice J Pennington, S Schoenholz, S Ganguli
Advances in neural information processing systems, 4785-4795, 2017
136 2017 The four-loop remainder function and multi-Regge behavior at NNLLA in planar LJ Dixon, JM Drummond, C Duhr, J Pennington
Journal of High Energy Physics 2014 (6), 116, 2014
127 2014 Single-valued harmonic polylogarithms and the multi-Regge limit LJ Dixon, C Duhr, J Pennington
Journal of High Energy Physics 2012 (10), 74, 2012
107 2012 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
103 2018 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
90 2013 Geometry of neural network loss surfaces via random matrix theory J Pennington, Y Bahri
International Conference on Machine Learning, 2798-2806, 2017
84 2017 A mean field theory of batch normalization G Yang, J Pennington, V Rao, J Sohl-Dickstein, SS Schoenholz
arXiv preprint arXiv:1902.08129, 2019
83 2019 Nonlinear random matrix theory for deep learning J Pennington, P Worah
Advances in Neural Information Processing Systems, 2637-2646, 2017
80 2017 The emergence of spectral universality in deep networks J Pennington, SS Schoenholz, S Ganguli
arXiv preprint arXiv:1802.09979, 2018
75 2018 Dynamical isometry and a mean field theory of RNNs: Gating enables signal propagation in recurrent neural networks M Chen, J Pennington, SS Schoenholz
arXiv preprint arXiv:1806.05394, 2018
66 2018 Spherical random features for polynomial kernels J Pennington, FXX Yu, S Kumar
Advances in Neural Information Processing Systems 28, 1846-1854, 2015
54 2015 Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) T Cohn, Y He, Y Liu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020
52 2020 