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 | 4704 | 2019 |
Neural Turing Machines A Graves arXiv preprint arXiv:1410.5401, 2014 | 3096 | 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 | 2507 | 2015 |
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 | 1944 | 2016 |
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 |
Neural scene representation and rendering SMA Eslami, D Jimenez Rezende, F Besse, F Viola, AS Morcos, ... Science 360 (6394), 1204-1210, 2018 | 722 | 2018 |
Video pixel networks N Kalchbrenner, A Oord, K Simonyan, I Danihelka, O Vinyals, A Graves, ... International Conference on Machine Learning, 1771-1779, 2017 | 493 | 2017 |
Grid long short-term memory N Kalchbrenner, I Danihelka, A Graves arXiv preprint arXiv:1507.01526, 2015 | 455 | 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 | 425 | 2017 |
Deep autoregressive networks K Gregor, I Danihelka, A Mnih, C Blundell, D Wierstra International Conference on Machine Learning, 1242-1250, 2014 | 350 | 2014 |
One-shot generalization in deep generative models D Rezende, I Danihelka, K Gregor, D Wierstra International conference on machine learning, 1521-1529, 2016 | 316 | 2016 |
Towards conceptual compression K Gregor, F Besse, D Jimenez Rezende, I Danihelka, D Wierstra Advances in neural information processing systems 29, 2016 | 279 | 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 | 271 | 2019 |
Memory-efficient backpropagation through time A Gruslys, R Munos, I Danihelka, M Lanctot, A Graves Advances in neural information processing systems 29, 2016 | 262* | 2016 |
Associative long short-term memory I Danihelka, G Wayne, B Uria, N Kalchbrenner, A Graves International conference on machine learning, 1986-1994, 2016 | 206 | 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 | 185 | 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 | 80 | 2021 |
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 |
Policy improvement by planning with Gumbel I Danihelka, A Guez, J Schrittwieser, D Silver International Conference on Learning Representations, 2022 | 61 | 2022 |
Causally correct partial models for reinforcement learning DJ Rezende, I Danihelka, G Papamakarios, NR Ke, R Jiang, T Weber, ... arXiv preprint arXiv:2002.02836, 2020 | 34 | 2020 |