| 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 | 64 | 2021 |
| Uncovering mesa-optimization algorithms in transformers J Von Oswald, M Schlegel, A Meulemans, S Kobayashi, E Niklasson, ... arXiv preprint arXiv:2309.05858, 2023 | 58* | 2023 |
| Beyond backpropagation: bilevel optimization through implicit differentiation and equilibrium propagation N Zucchet, J Sacramento Neural Computation 34 (12), 2022 | 32 | 2022 |
| 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, 2022 | 28 | 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 | 26 | 2022 |
| A contrastive rule for meta-learning N Zucchet, S Schug, J Von Oswald, D Zhao, J Sacramento Advances in Neural Information Processing Systems, 2022 | 24 | 2022 |
| Online learning of long-range dependencies N Zucchet, R Meier, S Schug, A Mujika, J Sacramento Advances in Neural Information Processing Systems, 2023 | 19 | 2023 |
| Recurrent neural networks: vanishing and exploding gradients are not the end of the story N Zucchet, A Orvieto Advances in Neural Information Processing Systems, 2024 | 12 | 2024 |
| 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 interplay between learning and memory in deep state space models J Smekal, N Zucchet, D Biderman, EK Buchanan, JTH Smith, ... | 1 | 2024 |
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