| 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 | 857 | 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 | 723 | 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 | 373 | 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 | 260 | 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 | 183 | 2019 |
| 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 | 123 | 2020 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 100 | 2022 |
| Dataset Distillation with Infinitely Wide Convolutional Networks T Nguyen, R Novak, L Xiao, J Lee Neural Information Processing Systems (NeurIPS) 2021, 2021 | 73 | 2021 |
| 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 | 64 | 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 | 32 | 2020 |
| Improving the Neural Algorithm of Artistic Style R Novak, Y Nikulin arXiv preprint arXiv:1605.04603, 2016 | 29 | 2016 |
| Exploring the Neural Algorithm of Artistic Style Y Nikulin, R Novak arXiv preprint arXiv:1602.07188, 2016 | 29 | 2016 |
| Iterative Refinement for Machine Translation R Novak, M Auli, D Grangier Bay Area Machine Learning Symposium (BayLearn) 2017, 2016 | 24 | 2016 |
| 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 | 19 | 2020 |
| Fast finite width neural tangent kernel R Novak, J Sohl-Dickstein, SS Schoenholz International Conference on Machine Learning (ICML) 2022, 2021 | 15 | 2021 |
| Fast Neural Kernel Embeddings for General Activations I Han, A Zandieh, J Lee, R Novak, L Xiao, A Karbasi Neural Information Processing Systems (NeurIPS) 2022, 2022 | 3 | 2022 |
| Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling J Hron, R Novak, J Pennington, J Sohl-Dickstein International Conference on Machine Learning (ICML) 2022, 2022 | 1 | 2022 |
Jascha Sohl-DicksteinGoogle BrainVerified email at google.com
