| Relational inductive biases, deep learning, and graph networks PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ... arXiv preprint arXiv:1806.01261, 2018 | 3806 | 2018 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 1493 | 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 | 425 | 2024 |
| Visual interaction networks: Learning a physics simulator from video N Watters, D Zoran, T Weber, P Battaglia, R Pascanu, A Tacchetti Advances in neural information processing systems 30, 2017 | 411 | 2017 |
| Relational inductive biases, deep learning, and graph networks. arXiv 2018 PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ... arXiv preprint arXiv:1806.01261, 2018 | 214 | 2018 |
| Unsupervised learning of invariant representations F Anselmi, JZ Leibo, L Rosasco, J Mutch, A Tacchetti, T Poggio Theoretical Computer Science 633, 112-121, 2016 | 149 | 2016 |
| Relational forward models for multi-agent learning A Tacchetti, HF Song, PAM Mediano, V Zambaldi, NC Rabinowitz, ... arXiv preprint arXiv:1809.11044, 2018 | 87 | 2018 |
| 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 | 85 | 2013 |
| Human-centred mechanism design with Democratic AI R Koster, J Balaguer, A Tacchetti, A Weinstein, T Zhu, O Hauser, ... Nature Human Behavior 6, 1398–1407, 2022 | 71 | 2022 |
| GURLS: A Least Squares Library for Supervised Learning A Tacchetti, P Mallapragada, M Santoro, R Rosasco Journal of Machine Learning Research 14, 3201-3205, 2013 | 57 | 2013 |
| Learning to play no-press diplomacy with best response policy iteration T Anthony, T Eccles, A Tacchetti, J Kramár, I Gemp, T Hudson, N Porcel, ... Advances in Neural Information Processing Systems 33, 17987-18003, 2020 | 52 | 2020 |
| Invariant recognition shapes neural representations of visual input A Tacchetti, L Isik, TA Poggio Annual review of vision science 4 (1), 403-422, 2018 | 46 | 2018 |
| Fast, invariant representation for human action in the visual system L Isik, A Tacchetti, T Poggio Journal of Neurophysiology 119 (2), 631-640, 2018 | 45 | 2018 |
| Negotiation and honesty in artificial intelligence methods for the board game of Diplomacy J Kramár, T Eccles, I Gemp, A Tacchetti, KR McKee, M Malinowski, ... Nature Communications 13 (1), 7214, 2022 | 44 | 2022 |
| The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work). T Poggio, J Mutch, J Leibo, L Rosasco, A Tacchetti | 42 | 2013 |
| A neural architecture for designing truthful and efficient auctions A Tacchetti, DJ Strouse, M Garnelo, T Graepel, Y Bachrach arXiv preprint arXiv:1907.05181 3 (3.6), 4, 2019 | 39* | 2019 |
| GURLS: a toolbox for large scale multiclass learning A Tacchetti, P Mallapragada, M Santoro, L Rosasco NIPS 2011 workshop on parallel and large-scale machine learning. http://cbcl …, 2011 | 30* | 2011 |
| Invariant recognition drives neural representations of action sequences A Tacchetti, L Isik, T Poggio PLoS computational biology 13 (12), e1005859, 2017 | 26* | 2017 |
| Graph neural network systems for behavior prediction and reinforcement learning in multple agent environments H Song, A Tacchetti, PW Battaglia, V Zambaldi US Patent App. 17/054,632, 2021 | 24 | 2021 |
| Magic materials: a theory of deep hierarchical architectures for learning sensory representations F Anselmi, JZ Leibo, L Rosasco, J Mutch, A Tacchetti, T Poggio CBCL paper 16, 2013 | 23 | 2013 |
