| 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 | 1260 | 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 | 1074 | 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 | 971 | 2022 |
| 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 | 481 | 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 | 368 | 2018 |
| Neural tangents: Fast and easy infinite neural networks in python R Novak, L Xiao, J Hron, J Lee, AA Alemi, J Sohl-Dickstein, ... arXiv preprint arXiv:1912.02803, 2019 | 252 | 2019 |
| Dataset distillation with infinitely wide convolutional networks T Nguyen, R Novak, L Xiao, J Lee Advances in Neural Information Processing Systems 34, 5186-5198, 2021 | 218 | 2021 |
| 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 | 206 | 2020 |
| Infinite attention: NNGP and NTK for deep attention networks J Hron, Y Bahri, J Sohl-Dickstein, R Novak International Conference on Machine Learning, 4376-4386, 2020 | 126 | 2020 |
| Fast finite width neural tangent kernel R Novak, J Sohl-Dickstein, SS Schoenholz International Conference on Machine Learning, 17018-17044, 2022 | 58 | 2022 |
| 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 | 54 | 2020 |
| Beyond human data: Scaling self-training for problem-solving with language models A Singh, JD Co-Reyes, R Agarwal, A Anand, P Patil, PJ Liu, J Harrison, ... arXiv preprint arXiv:2312.06585, 2023 | 41 | 2023 |
| Exploring the neural algorithm of artistic style Y Nikulin, R Novak arXiv preprint arXiv:1602.07188, 2016 | 37 | 2016 |
| Improving the neural algorithm of artistic style R Novak, Y Nikulin arXiv preprint arXiv:1605.04603, 2016 | 35 | 2016 |
| Exact posterior distributions of wide Bayesian neural networks J Hron, Y Bahri, R Novak, J Pennington, J Sohl-Dickstein arXiv preprint arXiv:2006.10541, 2020 | 31 | 2020 |
| Iterative refinement for machine translation R Novak, M Auli, D Grangier arXiv preprint arXiv:1610.06602, 2016 | 30 | 2016 |
| 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 | 26 | 2023 |
| Fast neural kernel embeddings for general activations I Han, A Zandieh, J Lee, R Novak, L Xiao, A Karbasi Advances in neural information processing systems 35, 35657-35671, 2022 | 14 | 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, 8926-8945, 2022 | 7 | 2022 |
| Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability J Hron, L Culp, G Elsayed, R Liu, B Adlam, M Bileschi, B Bohnet, ... arXiv preprint arXiv:2408.07852, 2024 | | 2024 |
Jascha Sohl-DicksteinAnthropicVerified email at anthropic.com
