| 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 | 1612 | 2023 |
| 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 | 412 | 2024 |
| Behaviour suite for reinforcement learning I Osband, Y Doron, M Hessel, J Aslanides, E Sezener, A Saraiva, ... International Conference on Learning Representations (ICLR), 2019 | 184 | 2019 |
| The DeepMind JAX Ecosystem I Babuschkin, K Baumli, A Bell, S Bhupatiraju, J Bruce, P Buchlovsky, ... URL http://github. com/deepmind 24, 25, 2020 | 155* | 2020 |
| Optax: composable gradient transformation and optimisation M Hessel, D Budden, F Viola, M Rosca, E Sezener, T Hennigan JAX, http://github. com/deepmind/optax (last access: 4 July 2023), version 0.0 1, 2020 | 86* | 2020 |
| Streamingqa: A benchmark for adaptation to new knowledge over time in question answering models A Liska, T Kocisky, E Gribovskaya, T Terzi, E Sezener, D Agrawal, ... International Conference on Machine Learning, 13604-13622, 2022 | 48 | 2022 |
| Gated Linear Networks J Veness, T Lattimore, A Bhoopchand, D Budden, C Mattern, ... AAAI 2021, 2019 | 43 | 2019 |
| A rapid and efficient learning rule for biological neural circuits E Sezener, A Grabska-Barwińska, D Kostadinov, M Beau, S Krishnagopal, ... BioRxiv, 2021.03. 10.434756, 2021 | 35 | 2021 |
| Optimizing the depth and the direction of prospective planning using information values E Sezener, A Dezfouli, M Keramati PLOS Computational Biology 15 (3), e1006827, 2019 | 31 | 2019 |
| Large-scale multilingual audio visual dubbing Y Yang, B Shillingford, Y Assael, M Wang, W Liu, Y Chen, Y Zhang, ... arXiv preprint arXiv:2011.03530, 2020 | 25 | 2020 |
| The DeepMind JAX Ecosystem, 2020 IB DeepMind, K Baumli, A Bell, S Bhupatiraju, J Bruce, P Buchlovsky, ... URL http://github. com/google-deepmind, 0 | 25 | |
| Gaussian Gated Linear Networks D Budden, A Marblestone, E Sezener, T Lattimore, G Wayne, J Veness NeurIPS 2020, 2020 | 14 | 2020 |
| Minimal sign representation of Boolean functions: algorithms and exact results for low dimensions E Sezener, E Oztop Neural Computation 27 (8), 1796-1823, 2015 | 11 | 2015 |
| Cyprien de Masson d’Autume, Tim Scholtes, Manzil Zaheer, Susannah Young, Ellen Gilsenan-McMahon, Sophia Austin, Phil Blunsom, and Angeliki Lazaridou. 2022 A Liška, T Kociský, E Gribovskaya, T Terzi, E Sezener, D Agrawal Streamingqa: A benchmark for adaptation to new knowledge over time in …, 0 | 11 | |
| Online Learning in Contextual Bandits using Gated Linear Networks E Sezener, M Hutter, D Budden, J Wang, J Veness NeurIPS 2020, 2020 | 9 | 2020 |
| Cyprien de Masson d’Autume, Tim Scholtes, Manzil Zaheer, Susannah Young, Ellen Gilsenan-McMahon, Sophia Austin, Phil Blunsom, and Angeliki Lazaridou. 2022. Streamingqa: A … A Liska, T Kociský, E Gribovskaya, T Terzi, E Sezener, D Agrawal International Conference on Machine Learning, 2022 | 8 | 2022 |
| A Combinatorial Perspective on Transfer Learning J Wang, E Sezener, D Budden, M Hutter, J Veness NeurIPS 2020, 2020 | 8 | 2020 |
| Inferring human values for Safe AGI design E Sezener Artificial General Intelligence (AGI) 9205, pp 152-155, 2015 | 6 | 2015 |
| Gated Linear Networks.(2019) J Veness, T Lattimore, D Budden, A Bhoopchand, C Mattern, ... | 3 | 1910 |
| Static and Dynamic Values of Computation in MCTS E Sezener, P Dayan Uncertainty in Artificial Intelligence (UAI), 2020 | 2 | 2020 |
