| Deep Neural Networks as Gaussian Processes J Lee, Y Bahri, R Novak, SS Schoenholz, J Pennington, J Sohl-Dickstein International Conference on Learning Representations (ICLR) 2018, 2017 | 370 | 2017 |
| 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, ... Advances in Neural Information Processing Systems (NeurIPS) 32, 8570 - 8581, 2019 | 246 | 2019 |
| Sensitivity and Generalization in Neural Networks: an Empirical Study R Novak, Y Bahri, DA Abolafia, J Pennington, J Sohl-Dickstein International Conference on Learning Representations (ICLR) 2018, 2018 | 195 | 2018 |
| Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes R Novak, L Xiao, J Lee, Y Bahri, G Yang, J Hron, D Abolafia, J Pennington, ... International Conference on Learning Representations (ICLR) 2019, 2018 | 105 | 2018 |
| Neural Tangents: Fast and Easy Infinite Neural Networks in Python R Novak, L Xiao, J Hron, J Lee, AA Alemi, J Sohl-Dickstein, ... International Conference on Learning Representations (ICLR) 2020, 2019 | 32 | 2019 |
| Improving the Neural Algorithm of Artistic Style R Novak, Y Nikulin arXiv preprint arXiv:1605.04603, 2016 | 21 | 2016 |
| Exploring the Neural Algorithm of Artistic Style Y Nikulin, R Novak arXiv preprint arXiv:1602.07188, 2016 | 21 | 2016 |
| Iterative Refinement for Machine Translation R Novak, M Auli, D Grangier Bay Area Machine Learning Symposium (BayLearn) 2017, 2016 | 14 | 2016 |
| Finite versus infinite neural networks: an empirical study J Lee, SS Schoenholz, J Pennington, B Adlam, L Xiao, R Novak, ... Neural Information Processing Systems (NeurIPS) 2020, 2020 | 8 | 2020 |
| On the infinite width limit of neural networks with a standard parameterization J Sohl-Dickstein, R Novak, SS Schoenholz, J Lee arXiv preprint arXiv:2001.07301, 2020 | 6 | 2020 |
| Infinite attention: NNGP and NTK for deep attention networks J Hron, Y Bahri, J Sohl-Dickstein, R Novak International Conference on Machine Learning (ICML) 2020, 2020 | 5 | 2020 |
| Exact posterior distributions of wide Bayesian neural networks J Hron, Y Bahri, R Novak, J Pennington, J Sohl-Dickstein ICML 2020 Workshop on Uncertainty & Robustness in Deep Learning; BayLearn 2020, 2020 | 1 | 2020 |
| 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, ... Journal of Statistical Mechanics: Theory and Experiment 2020 (12), 124002, 2020 | | 2020 |
Jascha Sohl-DicksteinGoogle BrainVerified email at google.com
