| Continual learning with hypernetworks J von Oswald, C Henning, BF Grewe, J Sacramento International Conference on Learning Representation (ICLR 2020), 2019 | 360 | 2019 |
| Transformers learn in-context by gradient descent J Von Oswald, E Niklasson, E Randazzo, J Sacramento, A Mordvintsev, ... International Conference on Machine Learning, 35151-35174, 2023 | 213 | 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, ... Advances in Neural Information Processing Systems 34, 5250-5263, 2021 | 48 | 2021 |
| 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 | 47 | 2021 |
| Continual learning in recurrent neural networks B Ehret, C Henning, MR Cervera, A Meulemans, J Von Oswald, BF Grewe arXiv preprint arXiv:2006.12109, 2020 | 44 | 2020 |
| Meta-Learning via Hypernetworks D Zhao, S Kobayashi, J Sacramento, J von Oswald 4th Workshop on Meta-Learning at NeurIPS 2020, Vancouver, Canada, 2020 | 42 | 2020 |
| Neural networks with late-phase weights J von Oswald, S Kobayashi, A Meulemans, C Henning, BF Grewe, ... International Conference on Learning Representation (ICLR 2021), arXiv: 2007 …, 2020 | 25 | 2020 |
| Approximating the predictive distribution via adversarially-trained hypernetworks C Henning, J von Oswald, J Sacramento, SC Surace, JP Pfister, ... Yarin, 2018 | 24 | 2018 |
| A contrastive rule for meta-learning N Zucchet, S Schug, J Von Oswald, D Zhao, J Sacramento Advances in Neural Information Processing Systems 35, 25921-25936, 2022 | 19 | 2022 |
| Random initialisations performing above chance and how to find them F Benzing, S Schug, R Meier, J Von Oswald, Y Akram, N Zucchet, ... arXiv preprint arXiv:2209.07509, 2022 | 19 | 2022 |
| Uncovering mesa-optimization algorithms in transformers J von Oswald, E Niklasson, M Schlegel, S Kobayashi, N Zucchet, ... arXiv preprint arXiv:2309.05858, 2023 | 15 | 2023 |
| 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 | 15 | 2022 |
| On the reversed bias-variance tradeoff in deep ensembles S Kobayashi, J von Oswald, BF Grewe ICML, 2021 | 7 | 2021 |
| 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 | 4 | 2023 |
| 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 | 1 | 2023 |
| Linear Transformers are Versatile In-Context Learners M Vladymyrov, J von Oswald, M Sandler, R Ge arXiv preprint arXiv:2402.14180, 2024 | | 2024 |
| Interpretability of Learning Algorithms Encoded in Deep Neural Networks J von Oswald ETH Zurich, 2024 | | 2024 |
| A complementary systems theory of meta-learning S Schug, N Zucchet, J von Oswald, J Sacramento Cosyne 2023, 2023 | | 2023 |
| 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 | | 2022 |
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