| Transformers learn in-context by gradient descent J Von Oswald, E Niklasson, E Randazzo, J Sacramento, A Mordvintsev, ... ICML 2023 (Oral), 2023 | 459 | 2023 |
| Continual learning with hypernetworks J von Oswald, C Henning, BF Grewe, J Sacramento ICLR 2020 (Spotlight), 2019 | 445 | 2019 |
| Posterior meta-replay for continual learning C Henning, M Cervera, F D'Angelo, J Von Oswald, R Traber, B Ehret, ... NeurIPS 2021, 2021 | 73 | 2021 |
| Continual learning in recurrent neural networks B Ehret, C Henning, MR Cervera, A Meulemans, J Von Oswald, BF Grewe ICLR 2021, 2020 | 61* | 2020 |
| Uncovering mesa-optimization algorithms in transformers J Von Oswald, M Schlegel, A Meulemans, S Kobayashi, E Niklasson, ... arXiv preprint arXiv:2309.05858, 2023 | 60* | 2023 |
| Learning where to learn: Gradient sparsity in meta and continual learning J Von Oswald, D Zhao, S Kobayashi, S Schug, M Caccia, N Zucchet, ... NeurIPS 2021, 2021 | 60 | 2021 |
| Meta-Learning via Hypernetworks D Zhao, S Kobayashi, J Sacramento, J von Oswald 4th Workshop on Meta-Learning at NeurIPS 2020, Vancouver, Canada, 2020 | 59 | 2020 |
| Neural networks with late-phase weights J von Oswald, S Kobayashi, A Meulemans, C Henning, BF Grewe, ... ICLR 2021, 2020 | 38 | 2020 |
| The least-control principle for local learning at equilibrium A Meulemans, N Zucchet, S Kobayashi, J Von Oswald, J Sacramento NeurIPS 2022 (Oral), 2022 | 27 | 2022 |
| Random initialisations performing above chance and how to find them F Benzing, S Schug, R Meier, J Von Oswald, Y Akram, N Zucchet, ... OPT2022: 14th Annual Workshop on Optimization for Machine Learning, 2022 | 25 | 2022 |
| Approximating the predictive distribution via adversarially-trained hypernetworks C Henning, J von Oswald, J Sacramento, SC Surace, JP Pfister, ... Bayesian Deep Learning Workshop, NeurIPS 2018 (Spotlight), 2018 | 25 | 2018 |
| A contrastive rule for meta-learning N Zucchet, S Schug, J Von Oswald, D Zhao, J Sacramento NeurIPS 2022, 2022 | 24 | 2022 |
| Discovering modular solutions that generalize compositionally S Schug, S Kobayashi, Y Akram, M Wołczyk, A Proca, J Von Oswald, ... ICLR 2024, 2023 | 12 | 2023 |
| 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 Workshop on Uncertainty and Robustness in Deep Learning, 2021 | 9 | 2021 |
| Adversarial robustness of in-context learning in transformers for linear regression U Anwar, J Von Oswald, L Kirsch, D Krueger, S Frei arXiv preprint arXiv:2411.05189, 2024 | 4 | 2024 |
| Linear Transformers are Versatile In-Context Learners M Vladymyrov, J Von Oswald, M Sandler, R Ge NeurIPS 2024, 2024 | 3 | 2024 |
| Weight decay induces low-rank attention layers S Kobayashi, Y Akram, J Von Oswald NeurIPS 2024, 2024 | 2 | 2024 |
| When can transformers compositionally generalize in-context? S Kobayashi, S Schug, Y Akram, F Redhardt, J von Oswald, R Pascanu, ... ICML 2024 Next Generation of Sequence Modeling Architectures Workshop, 2024 | 1 | 2024 |
| Disentangling the predictive variance of deep ensembles through the neural tangent kernel S Kobayashi, P Vilimelis Aceituno, J Von Oswald NeurIPS 2022, 2022 | 1 | 2022 |
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