A theoretical framework for target propagation A Meulemans, F Carzaniga, J Suykens, J Sacramento, BF Grewe Advances in Neural Information Processing Systems 33, 2020 | 68 | 2020 |
Continual Learning in Recurrent Neural Networks B Ehret, C Henning, MR Cervera, A Meulemans, J von Oswald, BF Grewe International Conference on Representation Learning (ICLR 2021), 2020 | 32 | 2020 |
Credit assignment in neural networks through deep feedback control A Meulemans, M Tristany Farinha, J García Ordóñez, P Vilimelis Aceituno, ... Advances in Neural Information Processing Systems 34, 4674-4687, 2021 | 28 | 2021 |
Neural networks with late-phase weights J von Oswald, S Kobayashi, J Sacramento, A Meulemans, C Henning, ... International Conference on Learning Representation (ICLR 2021), arXiv: 2007 …, 2020 | 26 | 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 | 16 | 2022 |
Minimizing control for credit assignment with strong feedback A Meulemans, MT Farinha, MR Cervera, J Sacramento, BF Grewe International Conference on Machine Learning, 15458-15483, 2022 | 9 | 2022 |
Challenges for Using Impact Regularizers to Avoid Negative Side Effects D Lindner, K Matoba, A Meulemans arXiv preprint arXiv:2101.12509, 2021 | 6 | 2021 |
Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis A Meulemans, S Schug, S Kobayashi, N Daw, G Wayne Advances in Neural Information Processing Systems 36, 2024 | 3 | 2024 |
Learning probability distributions of sensory inputs with Monte Carlo Predictive Coding G Oliviers, R Bogacz, A Meulemans bioRxiv, 2024.02. 29.581455, 2024 | | 2024 |
Towards a mathematical understanding of biologically plausible learning methods for deep neural networks A Meulemans | | |