| 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 | 1583 | 2023 |
| The marabou framework for verification and analysis of deep neural networks G Katz, DA Huang, D Ibeling, K Julian, C Lazarus, R Lim, P Shah, ... Computer Aided Verification: 31st International Conference, CAV 2019, New …, 2019 | 633 | 2019 |
| Large-scale representation learning on graphs via bootstrapping S Thakoor, C Tallec, MG Azar, M Azabou, EL Dyer, R Munos, P Veličković, ... arXiv preprint arXiv:2102.06514, 2021 | 447* | 2021 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 395 | 2024 |
| Adversarial examples for natural language classification problems V Kuleshov, S Thakoor, T Lau, S Ermon | 98 | 2018 |
| 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 | 69 | 2020 |
| Byol-explore: Exploration by bootstrapped prediction Z Guo, S Thakoor, M Pîslar, B Avila Pires, F Altché, C Tallec, A Saade, ... Advances in neural information processing systems 35, 31855-31870, 2022 | 64 | 2022 |
| Gemma 2: Improving open language models at a practical size G Team, M Riviere, S Pathak, PG Sessa, C Hardin, S Bhupatiraju, ... arXiv preprint arXiv:2408.00118, 2024 | 59 | 2024 |
| 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 |
| Large-scale graph representation learning with very deep gnns and self-supervision R Addanki, PW Battaglia, D Budden, A Deac, J Godwin, T Keck, WLS Li, ... arXiv preprint arXiv:2107.09422, 2021 | 30 | 2021 |
| Understanding self-predictive learning for reinforcement learning Y Tang, ZD Guo, PH Richemond, BA Pires, Y Chandak, R Munos, ... International Conference on Machine Learning, 33632-33656, 2023 | 28 | 2023 |
| Learning to play Othello without human knowledge S Thakoor, S Nair, M Jhunjhunwala Stanford University, Final Project Report, 2016 | 21 | 2016 |
| Half-Hop: A graph upsampling approach for slowing down message passing M Azabou, V Ganesh, S Thakoor, CH Lin, L Sathidevi, R Liu, M Valko, ... International Conference on Machine Learning, 1341-1360, 2023 | 15 | 2023 |
| Generalised policy improvement with geometric policy composition S Thakoor, M Rowland, D Borsa, W Dabney, R Munos, A Barreto International Conference on Machine Learning, 21272-21307, 2022 | 6 | 2022 |
| Relax, it doesn’t matter how you get there: A new self-supervised approach for multi-timescale behavior analysis M Azabou, M Mendelson, N Ahad, M Sorokin, S Thakoor, C Urzay, E Dyer Advances in Neural Information Processing Systems 36, 2024 | 4 | 2024 |
| Learning behavior representations through multi-timescale bootstrapping M Azabou, M Mendelson, M Sorokin, S Thakoor, N Ahad, C Urzay, ... arXiv preprint arXiv:2206.07041, 2022 | 3 | 2022 |
| Blade: Robust exploration via diffusion models B Piot, ZD Guo, S Thakoor, MG Azar Deep Reinforcement Learning Workshop NeurIPS 2022, 2022 | 3 | 2022 |
| Synthesis of programs from multimodal datasets S Thakoor, S Shah, G Ramakrishnan, A Sanyal Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 3 | 2018 |
| Representations and exploration for deep reinforcement learning using singular value decomposition Y Chandak, S Thakoor, ZD Guo, Y Tang, R Munos, W Dabney, DL Borsa International Conference on Machine Learning, 4009-4034, 2023 | 2 | 2023 |
| Quantifying and Understanding Adversarial Examples in Discrete Input Spaces V Kuleshov, E Nikishin, S Thakoor, T Lau, S Ermon arXiv preprint arXiv:2112.06276, 2021 | | 2021 |
