| 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 | 1038 | 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 | 895 | 2019 |
| 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 | 454 | 2022 |
| 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 | 437 | 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 | 314 | 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 | 220 | 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 | 161 | 2020 |
| Dataset Distillation with Infinitely Wide Convolutional Networks T Nguyen, R Novak, L Xiao, J Lee Neural Information Processing Systems (NeurIPS) 2021, 2021 | 143 | 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 | 97 | 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 | 43 | 2020 |
| Fast finite width neural tangent kernel R Novak, J Sohl-Dickstein, SS Schoenholz International Conference on Machine Learning (ICML) 2022, 2021 | 34 | 2021 |
| Improving the Neural Algorithm of Artistic Style R Novak, Y Nikulin arXiv preprint arXiv:1605.04603, 2016 | 30 | 2016 |
| Exploring the Neural Algorithm of Artistic Style Y Nikulin, R Novak arXiv preprint arXiv:1602.07188, 2016 | 30 | 2016 |
| Iterative Refinement for Machine Translation R Novak, M Auli, D Grangier Bay Area Machine Learning Symposium (BayLearn) 2017, 2016 | 27 | 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 | 26 | 2020 |
| 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 | 8 | 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 | 4 | 2022 |
| Frontier Language Models are not Robust to Adversarial Arithmetic, or" What do I need to say so you agree 2+ 2= 5? CD Freeman, L Culp, A Parisi, ML Bileschi, GF Elsayed, A Rizkowsky, ... arXiv preprint arXiv:2311.07587, 2023 | | 2023 |
| Small-scale proxies for large-scale Transformer training instabilities M Wortsman, PJ Liu, L Xiao, K Everett, A Alemi, B Adlam, JD Co-Reyes, ... arXiv preprint arXiv:2309.14322, 2023 | | 2023 |
Jascha Sohl-DicksteinGoogle BrainVerified email at google.com
