| A deep learning framework for neuroscience BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ... Nature Neuroscience 22 (11), 1761-1770, 2019 | 1116 | 2019 |
| Transformers learn in-context by gradient descent J von Oswald, E Niklasson, E Randazzo, J Sacramento, A Mordvintsev, ... International Conference on Machine Learning (ICML 2023), 2022 | 481 | 2022 |
| Continual learning with hypernetworks J von Oswald, C Henning, BF Grewe, J Sacramento International Conference on Learning Representations (ICLR 2020), 2019 | 455 | 2019 |
| Dendritic cortical microcircuits approximate the backpropagation algorithm J Sacramento, RP Costa, Y Bengio, W Senn Advances in Neural Information Processing Systems 31, 2018 | 383 | 2018 |
| A Theoretical Framework for Target Propagation A Meulemans, FS Carzaniga, JAK Suykens, J Sacramento, BF Grewe Advances in Neural Information Processing Systems 33, 2020 | 84 | 2020 |
| Posterior Meta-Replay for Continual Learning C Henning, MR Cervera, F D'Angelo, J von Oswald, R Traber, B Ehret, ... Advances in Neural Information Processing Systems 34, 2021 | 75 | 2021 |
| 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 |
| Meta-learning via hypernetworks D Zhao, S Kobayashi, J Sacramento, J von Oswald NeurIPS Workshop on Meta-learning 2020, 2020 | 61 | 2020 |
| 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, 2021 | 60 | 2021 |
| Dendritic error backpropagation in deep cortical microcircuits J Sacramento, RP Costa, Y Bengio, W Senn arXiv preprint arXiv:1801.00062, 2017 | 52 | 2017 |
| Credit Assignment in Neural Networks through Deep Feedback Control A Meulemans, MT Farinha, JG Ordóñez, PV Aceituno, J Sacramento, ... Advances in Neural Information Processing Systems 34, 2021 | 44 | 2021 |
| Neural networks with late-phase weights J von Oswald, S Kobayashi, A Meulemans, C Henning, BF Grewe, ... International Conference on Learning Representations (ICLR 2021), 2020 | 38 | 2020 |
| Beyond backpropagation: bilevel optimization through implicit differentiation and equilibrium propagation N Zucchet, J Sacramento Neural Computation, 2022 | 32 | 2022 |
| Computational roles of plastic probabilistic synapses M Llera-Montero, J Sacramento, RP Costa Current Opinion in Neurobiology 54, 90-97, 2019 | 30 | 2019 |
| 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, 2022 | 27 | 2022 |
| A neuronal least-action principle for real-time learning in cortical circuits W Senn, D Dold, A Kungl, B Ellenberger, J Jordan, Y Bengio, ... eLife, 2024;12:RP89674, 2024 | 26* | 2024 |
| Sensory representation of an auditory cued tactile stimulus in the posterior parietal cortex of the mouse H Mohan, Y Gallero-Salas, S Carta, J Sacramento, B Laurenczy, ... Scientific reports 8 (1), 7739, 2018 | 25 | 2018 |
| Approximating the predictive distribution via adversarially-trained hypernetworks C Henning, J von Oswald, J Sacramento, SC Surace, JP Pfister, ... NeurIPS Bayesian Deep Learning Workshop 2018, 2018 | 25 | 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, 2022 | 24 | 2022 |
| Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible Y Bengio, B Scellier, O Bilaniuk, J Sacramento, W Senn arXiv preprint arXiv:1606.01651, 2016 | 23 | 2016 |
Johannes von OswaldResearch Scientist, GoogleVerifisert e-postadresse på google.com
