| Behaviour suite for reinforcement learning I Osband, Y Doron, M Hessel, J Aslanides, E Sezener, A Saraiva, ... International Conference on Learning Representations (ICLR), 2019 | 100 | 2019 |
| Optax: composable gradient transformation and optimisation M Hessel, D Budden, F Viola, M Rosca, E Sezener, T Hennigan JAX, 2020 | 42* | 2020 |
| Gated Linear Networks J Veness, T Lattimore, A Bhoopchand, D Budden, C Mattern, ... AAAI 2021, 2019 | 18 | 2019 |
| The DeepMind JAX Ecosystem, 2020 I Babuschkin, K Baumli, A Bell, S Bhupatiraju, J Bruce, P Buchlovsky, ... URL http://github. com/deepmind, 2020 | 16* | 2020 |
| 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 | 16 | 2019 |
| A rapid and efficient learning rule for biological neural circuits E Sezener, A Grabska-Barwińska, D Kostadinov, M Beau, S Krishnagopal, ... BioRxiv, 2021 | 14 | 2021 |
| Minimal sign representation of Boolean functions: algorithms and exact results for low dimensions E Sezener, E Oztop Neural Computation 27 (8), 1796-1823, 2015 | 10 | 2015 |
| Online Learning in Contextual Bandits using Gated Linear Networks E Sezener, M Hutter, D Budden, J Wang, J Veness NeurIPS 2020, 2020 | 6 | 2020 |
| Inferring human values for Safe AGI design E Sezener Artificial General Intelligence (AGI) 9205, pp 152-155, 2015 | 6 | 2015 |
| The DeepMind JAX Ecosystem I Babuschkin, K Baumli, A Bell, S Bhupatiraju, J Bruce, P Buchlovsky, ... URL http://github. com/deepmind 24, 25, 2020 | 5 | 2020 |
| Gaussian Gated Linear Networks D Budden, A Marblestone, E Sezener, T Lattimore, G Wayne, J Veness NeurIPS 2020, 2020 | 4 | 2020 |
| 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 | 3 | 2020 |
| A Combinatorial Perspective on Transfer Learning J Wang, E Sezener, D Budden, M Hutter, J Veness NeurIPS 2020, 2020 | 3 | 2020 |
| Static and Dynamic Values of Computation in MCTS E Sezener, P Dayan Uncertainty in Artificial Intelligence (UAI), 2020 | 2 | 2020 |
| Heuristic algorithms for obtaining Polynomial Threshold Functions with low densities E Sezener, E Oztop arXiv preprint arXiv:1504.01167, 2015 | 2 | 2015 |
| StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models A Liška, T Kočiský, E Gribovskaya, T Terzi, E Sezener, D Agrawal, ... arXiv preprint arXiv:2205.11388, 2022 | 1 | 2022 |
| Computing the Value of Computation for Planning E Sezener arXiv preprint arXiv:1811.03035, 2018 | | 2018 |
| Algorithms for Obtaining Parsimonious Higher Order Neurons E Sezener, E Oztop International Conference on Artificial Neural Networks (ICANN), 146-154, 2017 | | 2017 |
| Approximate planning from better bounds on Q E Sezener, P Dayan Reinforcement Learning and Decision Making (RLDM) 2017, 2017 | | 2017 |
