| 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 = 4 super-Yang-Mills theory 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 |
