Towards biologically plausible deep learning Y Bengio, DH Lee, J Bornschein, T Mesnard, Z Lin arXiv preprint arXiv:1502.04156, 2015 | 429 | 2015 |
Rlaif: Scaling reinforcement learning from human feedback with ai feedback H Lee, S Phatale, H Mansoor, K Lu, T Mesnard, C Bishop, V Carbune, ... arXiv preprint arXiv:2309.00267, 2023 | 238 | 2023 |
Gemma: Open models based on gemini research and technology G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ... arXiv preprint arXiv:2403.08295, 2024 | 220 | 2024 |
An objective function for STDP Y Bengio, T Mesnard, A Fischer, S Zhang, Y Wu arXiv preprint arXiv:1509.05936 5 (6.2), 6.3, 2015 | 182* | 2015 |
Hindsight credit assignment A Harutyunyan, W Dabney, T Mesnard, M Gheshlaghi Azar, B Piot, ... Advances in neural information processing systems 32, 2019 | 96 | 2019 |
Counterfactual credit assignment in model-free reinforcement learning T Mesnard, T Weber, F Viola, S Thakoor, A Saade, A Harutyunyan, ... arXiv preprint arXiv:2011.09464, 2020 | 66 | 2020 |
Generalization of equilibrium propagation to vector field dynamics B Scellier, A Goyal, J Binas, T Mesnard, Y Bengio arXiv preprint arXiv:1808.04873, 2018 | 47* | 2018 |
Nash learning from human feedback R Munos, M Valko, D Calandriello, MG Azar, M Rowland, ZD Guo, Y Tang, ... arXiv preprint arXiv:2312.00886, 2023 | 43 | 2023 |
Geometric entropic exploration ZD Guo, MG Azar, A Saade, S Thakoor, B Piot, BA Pires, M Valko, ... arXiv preprint arXiv:2101.02055, 2021 | 40 | 2021 |
Direct language model alignment from online ai feedback S Guo, B Zhang, T Liu, T Liu, M Khalman, F Llinares, A Rame, T Mesnard, ... arXiv preprint arXiv:2402.04792, 2024 | 32 | 2024 |
Towards deep learning with spiking neurons in energy based models with contrastive hebbian plasticity T Mesnard, W Gerstner, J Brea arXiv preprint arXiv:1612.03214, 2016 | 27 | 2016 |
Curiosity in hindsight: Intrinsic exploration in stochastic environments D Jarrett, C Tallec, F Altché, T Mesnard, R Munos, M Valko | 13 | 2023 |
Ghost units yield biologically plausible backprop in deep neural networks T Mesnard, G Vignoud, J Sacramento, W Senn, Y Bengio arXiv preprint arXiv:1911.08585, 2019 | 6 | 2019 |
A survey of temporal credit assignment in deep reinforcement learning E Pignatelli, J Ferret, M Geist, T Mesnard, H van Hasselt, L Toni arXiv preprint arXiv:2312.01072, 2023 | 4 | 2023 |
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models A Botev, S De, SL Smith, A Fernando, GC Muraru, R Haroun, L Berrada, ... arXiv preprint arXiv:2404.07839, 2024 | 2 | 2024 |
Quantile credit assignment T Mesnard, W Chen, A Saade, Y Tang, M Rowland, T Weber, C Lyle, ... International Conference on Machine Learning, 24517-24531, 2023 | 2 | 2023 |
Activation alignment: exploring the use of approximate activity gradients in multilayer networks T Mesnard, B Richards 2018 Conference on Cognitive Computational Neuroscience, Brentwood …, 2018 | 1 | 2018 |
Connectionist Temporal Classification: Labelling Unsegmented Sequences with Recurrent Neural Networks A AUVOLAT, T MESNARD | 1 | 2006 |
Credit Assignment in Deep Reinforcement Learning T Mesnard Institut Polytechnique de Paris, 2023 | | 2023 |