Glove: Global vectors for word representation J Pennington, R Socher, CD Manning
Proceedings of the 2014 conference on empirical methods in natural language …, 2014
30471 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
1599 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
1057 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
736 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 32, 2019
598 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
327 2018 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
248 2018 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
226 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 30, 2017
208 2017 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
186 2013 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), 1-59, 2014
164 2014 Nonlinear random matrix theory for deep learning J Pennington, P Worah
Advances in neural information processing systems 30, 2017
152 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
143 2019 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
143 2012 Geometry of neural network loss surfaces via random matrix theory J Pennington, Y Bahri
International Conference on Machine Learning, 2798-2806, 2017
130 2017 Statistical mechanics of deep learning Y Bahri, J Kadmon, J Pennington, SS Schoenholz, J Sohl-Dickstein, ...
Annual Review of Condensed Matter Physics 11 (1), 2020
126 2020 The emergence of spectral universality in deep networks J Pennington, S Schoenholz, S Ganguli
International Conference on Artificial Intelligence and Statistics, 1924-1932, 2018
124 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), 1-58, 2013
111 2013 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
90 2020 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
89 2018 