| Reinforcement learning with unsupervised auxiliary tasks M Jaderberg, V Mnih, WM Czarnecki, T Schaul, JZ Leibo, D Silver, ... arXiv preprint arXiv:1611.05397, 2016 | 469 | 2016 |
| Learning to reinforcement learn JX Wang, Z Kurth-Nelson, D Tirumala, H Soyer, JZ Leibo, R Munos, ... arXiv preprint arXiv:1611.05763, 2016 | 243 | 2016 |
| Multi-agent reinforcement learning in sequential social dilemmas JZ Leibo, V Zambaldi, M Lanctot, J Marecki, T Graepel arXiv preprint arXiv:1702.03037, 2017 | 209 | 2017 |
| Deep q-learning from demonstrations T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, D Horgan, ... Thirty-Second AAAI Conference on Artificial Intelligence, 2018 | 208 | 2018 |
| Deepmind lab C Beattie, JZ Leibo, D Teplyashin, T Ward, M Wainwright, H Küttler, ... arXiv preprint arXiv:1612.03801, 2016 | 194 | 2016 |
| The dynamics of invariant object recognition in the human visual system L Isik, EM Meyers, JZ Leibo, T Poggio Journal of neurophysiology 111 (1), 91-102, 2014 | 154 | 2014 |
| Human-level performance in 3D multiplayer games with population-based reinforcement learning M Jaderberg, WM Czarnecki, I Dunning, L Marris, G Lever, AG Castaneda, ... Science 364 (6443), 859-865, 2019 | 130 | 2019 |
| Model-free episodic control C Blundell, B Uria, A Pritzel, Y Li, A Ruderman, JZ Leibo, J Rae, ... arXiv preprint arXiv:1606.04460, 2016 | 124* | 2016 |
| Value-decomposition networks for cooperative multi-agent learning P Sunehag, G Lever, A Gruslys, WM Czarnecki, V Zambaldi, M Jaderberg, ... arXiv preprint arXiv:1706.05296, 2017 | 102* | 2017 |
| Prefrontal cortex as a meta-reinforcement learning system JX Wang, Z Kurth-Nelson, D Kumaran, D Tirumala, H Soyer, JZ Leibo, ... Nature neuroscience 21 (6), 860-868, 2018 | 96 | 2018 |
| Learning from demonstrations for real world reinforcement learning T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, A Sendonaris, ... arXiv preprint arXiv:1704.03732, 4565-4573, 2017 | 84 | 2017 |
| Using fast weights to attend to the recent past J Ba, GE Hinton, V Mnih, JZ Leibo, C Ionescu Advances in Neural Information Processing Systems, 4331-4339, 2016 | 77 | 2016 |
| Unsupervised learning of invariant representations in hierarchical architectures F Anselmi, JZ Leibo, L Rosasco, J Mutch, A Tacchetti, T Poggio arXiv preprint arXiv:1311.4158, 2013 | 68 | 2013 |
| Unsupervised predictive memory in a goal-directed agent G Wayne, CC Hung, D Amos, M Mirza, A Ahuja, A Grabska-Barwinska, ... arXiv preprint arXiv:1803.10760, 2018 | 60 | 2018 |
| How important is weight symmetry in backpropagation? Q Liao, JZ Leibo, T Poggio Thirtieth AAAI Conference on Artificial Intelligence, 2016 | 59 | 2016 |
| A multi-agent reinforcement learning model of common-pool resource appropriation J Perolat, JZ Leibo, V Zambaldi, C Beattie, K Tuyls, T Graepel Advances in Neural Information Processing Systems, 3643-3652, 2017 | 57 | 2017 |
| Learning invariant representations and applications to face verification Q Liao, JZ Leibo, T Poggio Advances in neural information processing systems, 3057-3065, 2013 | 55 | 2013 |
| Unsupervised learning of invariant representations F Anselmi, JZ Leibo, L Rosasco, J Mutch, A Tacchetti, T Poggio Theoretical Computer Science 633, 112-121, 2016 | 54 | 2016 |
| Learning and disrupting invariance in visual recognition with a temporal association rule L Isik, JZ Leibo, T Poggio Frontiers in computational neuroscience 6, 37, 2012 | 44 | 2012 |
| Why the brain separates face recognition from object recognition JZ Leibo, J Mutch, T Poggio Advances in neural information processing systems, 711-719, 2011 | 40 | 2011 |
TOMASO POGGIOMcDermott Professor in Brain Sciences, MITVerified email at ai.mit.edu
