| Wheel defect detection with machine learning G Krummenacher, CS Ong, S Koller, S Kobayashi, JM Buhmann IEEE Transactions on Intelligent Transportation Systems 19 (4), 1176-1187, 2017 | 277 | 2017 |
| Comparison of hepatic resection and radiofrequency ablation for small hepatocellular carcinoma: a meta-analysis of 16,103 patients Q Xu, S Kobayashi, X Ye, X Meng Scientific reports 4 (1), 7252, 2014 | 144 | 2014 |
| Posterior meta-replay for continual learning C Henning, M Cervera, F D'Angelo, J Von Oswald, R Traber, B Ehret, ... Advances in neural information processing systems 34, 14135-14149, 2021 | 75 | 2021 |
| Learning where to learn: Gradient sparsity in meta and continual learning J Von Oswald, D Zhao, S Kobayashi, S Schug, M Caccia, N Zucchet, ... Advances in Neural Information Processing Systems 34, 5250-5263, 2021 | 63 | 2021 |
| Meta-learning via hypernetworks D Zhao, S Kobayashi, J Sacramento, J von Oswald IEEE, 2020 | 63 | 2020 |
| Uncovering mesa-optimization algorithms in transformers J Von Oswald, M Schlegel, A Meulemans, S Kobayashi, E Niklasson, ... arXiv preprint arXiv:2309.05858, 2023 | 62* | 2023 |
| Neural networks with late-phase weights J Von Oswald, S Kobayashi, A Meulemans, C Henning, BF Grewe, ... arXiv preprint arXiv:2007.12927, 2020 | 38 | 2020 |
| The least-control principle for local learning at equilibrium A Meulemans, N Zucchet, S Kobayashi, J Von Oswald, J Sacramento Advances in Neural Information Processing Systems 35, 33603-33617, 2022 | 28 | 2022 |
| Discovering modular solutions that generalize compositionally S Schug, S Kobayashi, Y Akram, M Wołczyk, A Proca, J Von Oswald, ... arXiv preprint arXiv:2312.15001, 2023 | 13 | 2023 |
| Meta-learning via classifier (-free) diffusion guidance E Nava, S Kobayashi, Y Yin, RK Katzschmann, BF Grewe arXiv preprint arXiv:2210.08942, 2022 | 11* | 2022 |
| Gated recurrent neural networks discover attention N Zucchet, S Kobayashi, Y Akram, J Von Oswald, M Larcher, A Steger, ... arXiv preprint arXiv:2309.01775, 2023 | 10 | 2023 |
| On the reversed bias-variance tradeoff in deep ensembles S Kobayashi, J von Oswald, BF Grewe ICML, 2021 | 9 | 2021 |
| Attention as a hypernetwork S Schug, S Kobayashi, Y Akram, J Sacramento, R Pascanu arXiv preprint arXiv:2406.05816, 2024 | 5 | 2024 |
| Would i have gotten that reward? long-term credit assignment by counterfactual contribution analysis A Meulemans, S Schug, S Kobayashi, G Wayne Advances in Neural Information Processing Systems 36, 68685-68735, 2023 | 5 | 2023 |
| Weight decay induces low-rank attention layers S Kobayashi, Y Akram, J Von Oswald Advances in Neural Information Processing Systems 37, 4481-4510, 2024 | 3 | 2024 |
| Multi-agent cooperation through learning-aware policy gradients A Meulemans, S Kobayashi, J Von Oswald, N Scherrer, E Elmoznino, ... arXiv preprint arXiv:2410.18636, 2024 | 1 | 2024 |
| When can transformers compositionally generalize in-context? S Kobayashi, S Schug, Y Akram, F Redhardt, J von Oswald, R Pascanu, ... arXiv preprint arXiv:2407.12275, 2024 | 1 | 2024 |
| Disentangling the predictive variance of deep ensembles through the neural tangent kernel S Kobayashi, P Vilimelis Aceituno, J Von Oswald Advances in Neural Information Processing Systems 35, 25335-25348, 2022 | 1 | 2022 |
| Learning Randomized Algorithms with Transformers J von Oswald, S Kobayashi, Y Akram, A Steger arXiv preprint arXiv:2408.10818, 2024 | | 2024 |
Johannes von OswaldResearch Scientist, GoogleVerified email at google.com
