| Grandmaster level in StarCraft II using multi-agent reinforcement learning O Vinyals, I Babuschkin, WM Czarnecki, M Mathieu, A Dudzik, J Chung, ... nature 575 (7782), 350-354, 2019 | 4864 | 2019 |
| Neural Turing Machines A Graves arXiv preprint arXiv:1410.5401, 2014 | 3121 | 2014 |
| Draw: A recurrent neural network for image generation K Gregor, I Danihelka, A Graves, D Rezende, D Wierstra International conference on machine learning, 1462-1471, 2015 | 2527 | 2015 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2070 | 2023 |
| Hybrid computing using a neural network with dynamic external memory A Graves, G Wayne, M Reynolds, T Harley, I Danihelka, ... Nature 538 (7626), 471-476, 2016 | 1974 | 2016 |
| Neural scene representation and rendering SMA Eslami, D Jimenez Rezende, F Besse, F Viola, AS Morcos, ... Science 360 (6394), 1204-1210, 2018 | 736 | 2018 |
| Video pixel networks N Kalchbrenner, A Oord, K Simonyan, I Danihelka, O Vinyals, A Graves, ... International Conference on Machine Learning, 1771-1779, 2017 | 503 | 2017 |
| Grid long short-term memory N Kalchbrenner, I Danihelka, A Graves arXiv preprint arXiv:1507.01526, 2015 | 459 | 2015 |
| The cramer distance as a solution to biased wasserstein gradients MG Bellemare, I Danihelka, W Dabney, S Mohamed, ... arXiv preprint arXiv:1705.10743, 2017 | 428 | 2017 |
| Deep autoregressive networks K Gregor, I Danihelka, A Mnih, C Blundell, D Wierstra International Conference on Machine Learning, 1242-1250, 2014 | 359 | 2014 |
| One-shot generalization in deep generative models D Rezende, I Danihelka, K Gregor, D Wierstra International conference on machine learning, 1521-1529, 2016 | 318 | 2016 |
| Towards conceptual compression K Gregor, F Besse, D Jimenez Rezende, I Danihelka, D Wierstra Advances in neural information processing systems 29, 2016 | 286 | 2016 |
| OpenSpiel: A framework for reinforcement learning in games M Lanctot, E Lockhart, JB Lespiau, V Zambaldi, S Upadhyay, J Pérolat, ... arXiv preprint arXiv:1908.09453, 2019 | 278 | 2019 |
| Memory-efficient backpropagation through time A Gruslys, R Munos, I Danihelka, M Lanctot, A Graves Advances in neural information processing systems 29, 2016 | 269* | 2016 |
| Associative long short-term memory I Danihelka, G Wayne, B Uria, N Kalchbrenner, A Graves International conference on machine learning, 1986-1994, 2016 | 208 | 2016 |
| Scaling memory-augmented neural networks with sparse reads and writes J Rae, JJ Hunt, I Danihelka, T Harley, AW Senior, G Wayne, A Graves, ... Advances in Neural Information Processing Systems 29, 2016 | 186 | 2016 |
| Muesli: Combining improvements in policy optimization M Hessel, I Danihelka, F Viola, A Guez, S Schmitt, L Sifre, T Weber, ... International conference on machine learning, 4214-4226, 2021 | 83 | 2021 |
| Policy improvement by planning with Gumbel I Danihelka, A Guez, J Schrittwieser, D Silver International Conference on Learning Representations, 2022 | 71 | 2022 |
| Comparison of maximum likelihood and gan-based training of real nvps I Danihelka, B Lakshminarayanan, B Uria, D Wierstra, P Dayan arXiv preprint arXiv:1705.05263, 2017 | 70 | 2017 |
| Learning by directional gradient descent D Silver, A Goyal, I Danihelka, M Hessel, H van Hasselt International Conference on Learning Representations, 2021 | 36 | 2021 |
