| Conditional neural processes M Garnelo, D Rosenbaum, C Maddison, T Ramalho, D Saxton, ... International conference on machine learning, 1704-1713, 2018 | 858 | 2018 |
| Neural scene representation and rendering SMA Eslami, D Jimenez Rezende, F Besse, F Viola, AS Morcos, ... Science 360 (6394), 1204-1210, 2018 | 758 | 2018 |
| Deep unsupervised clustering with gaussian mixture variational autoencoders N Dilokthanakul, PAM Mediano, M Garnelo, MCH Lee, H Salimbeni, ... arXiv preprint arXiv:1611.02648, 2016 | 746 | 2016 |
| Neural processes M Garnelo, J Schwarz, D Rosenbaum, F Viola, DJ Rezende, SM Eslami, ... arXiv preprint arXiv:1807.01622, 2018 | 681 | 2018 |
| Attentive neural processes H Kim, A Mnih, J Schwarz, M Garnelo, A Eslami, D Rosenbaum, O Vinyals, ... arXiv preprint arXiv:1901.05761, 2019 | 492 | 2019 |
| Interaction between tumour-infiltrating B cells and T cells controls the progression of hepatocellular carcinoma M Garnelo, A Tan, Z Her, J Yeong, CJ Lim, J Chen, KH Lim, A Weber, ... Gut 66 (2), 342-351, 2017 | 462 | 2017 |
| Reconciling deep learning with symbolic artificial intelligence: representing objects and relations M Garnelo, M Shanahan Current Opinion in Behavioral Sciences 29, 17-23, 2019 | 333 | 2019 |
| Towards deep symbolic reinforcement learning M Garnelo, K Arulkumaran, M Shanahan Deep Reinforcement Learning Workshop at the 30th Conference on Neural …, 2016 | 273 | 2016 |
| Open-ended learning in symmetric zero-sum games D Balduzzi, M Garnelo, Y Bachrach, W Czarnecki, J Perolat, M Jaderberg, ... International Conference on Machine Learning, 434-443, 2019 | 204 | 2019 |
| Game Plan: What AI can do for Football, and What Football can do for AI K Tuyls, S Omidshafiei, P Muller, Z Wang, J Connor, D Hennes, I Graham, ... Journal of Artificial Intelligence Research 71, 41-88, 2021 | 112 | 2021 |
| Adaptive posterior learning: few-shot learning with a surprise-based memory module T Ramalho, M Garnelo arXiv preprint arXiv:1902.02527, 2019 | 106 | 2019 |
| An explicitly relational neural network architecture M Shanahan, K Nikiforou, A Creswell, C Kaplanis, D Barrett, M Garnelo International Conference on Machine Learning, 8593-8603, 2020 | 80 | 2020 |
| Empirical evaluation of neural process objectives TA Le, H Kim, M Garnelo, D Rosenbaum, J Schwarz, YW Teh NeurIPS workshop on Bayesian Deep Learning 4, 2018 | 52 | 2018 |
| Inferring a continuous distribution of atom coordinates from cryo-EM images using VAEs D Rosenbaum, M Garnelo, M Zielinski, C Beattie, E Clancy, A Huber, ... arXiv preprint arXiv:2106.14108, 2021 | 49 | 2021 |
| Eagle and finch: Rwkv with matrix-valued states and dynamic recurrence B Peng, D Goldstein, Q Anthony, A Albalak, E Alcaide, S Biderman, ... arXiv preprint arXiv:2404.05892 3, 2024 | 45 | 2024 |
| Consistent generative query networks A Kumar, SM Eslami, DJ Rezende, M Garnelo, F Viola, E Lockhart, ... arXiv preprint arXiv:1807.02033, 2018 | 43 | 2018 |
| 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 | 35 | 2019 |
| Meta-learning surrogate models for sequential decision making A Galashov, J Schwarz, H Kim, M Garnelo, D Saxton, P Kohli, SM Eslami, ... arXiv preprint arXiv:1903.11907, 2019 | 33 | 2019 |
| Verification of deep probabilistic models K Dvijotham, M Garnelo, A Fawzi, P Kohli arXiv preprint arXiv:1812.02795, 2018 | 30 | 2018 |
| Pick your battles: Interaction graphs as population-level objectives for strategic diversity M Garnelo, WM Czarnecki, S Liu, D Tirumala, J Oh, G Gidel, ... arXiv preprint arXiv:2110.04041, 2021 | 28 | 2021 |
S. M. Ali EslamiGoogle DeepMindยืนยันอีเมลแล้วที่ google.com