| Teaching machines to read and comprehend KM Hermann, T Kocisky, E Grefenstette, L Espeholt, W Kay, M Suleyman, ... Advances in neural information processing systems 28, 2015 | 3635 | 2015 |
| Conditional image generation with pixelcnn decoders A Van den Oord, N Kalchbrenner, L Espeholt, O Vinyals, A Graves Advances in neural information processing systems 29, 2016 | 2412 | 2016 |
| Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures L Espeholt, H Soyer, R Munos, K Simonyan, V Mnih, T Ward, Y Doron, ... International conference on machine learning, 1407-1416, 2018 | 1418 | 2018 |
| Neural machine translation in linear time N Kalchbrenner, L Espeholt, K Simonyan, A Oord, A Graves, ... arXiv preprint arXiv:1610.10099, 2016 | 606 | 2016 |
| Multi-task deep reinforcement learning with popart M Hessel, H Soyer, L Espeholt, W Czarnecki, S Schmitt, H Van Hasselt Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3796-3803, 2019 | 266 | 2019 |
| Google research football: A novel reinforcement learning environment K Kurach, A Raichuk, P Stańczyk, M Zając, O Bachem, L Espeholt, ... Proceedings of the AAAI conference on artificial intelligence 34 (04), 4501-4510, 2020 | 262 | 2020 |
| Metnet: A neural weather model for precipitation forecasting CK Sønderby, L Espeholt, J Heek, M Dehghani, A Oliver, T Salimans, ... arXiv preprint arXiv:2003.12140, 2020 | 232 | 2020 |
| Seed rl: Scalable and efficient deep-rl with accelerated central inference L Espeholt, R Marinier, P Stanczyk, K Wang, M Michalski arXiv preprint arXiv:1910.06591, 2019 | 124 | 2019 |
| Deep learning for twelve hour precipitation forecasts L Espeholt, S Agrawal, C Sønderby, M Kumar, J Heek, C Bromberg, ... Nature communications 13 (1), 1-10, 2022 | 112 | 2022 |
| Processing sequences using convolutional neural networks AGA van den Oord, SEL Dieleman, NE Kalchbrenner, K Simonyan, ... US Patent 11,080,591, 2021 | 62 | 2021 |
| Oord, A. vd; Graves, A.; and Kavukcuoglu, K. 2016. Neural machine translation in linear time N Kalchbrenner, L Espeholt, K Simonyan arXiv preprint arXiv:1610.10099, 0 | 51 | |
| MetNet: A neural weather model for precipitation forecasting C Kaae Sønderby, L Espeholt, J Heek, M Dehghani, A Oliver, T Salimans, ... arXiv e-prints, arXiv: 2003.12140, 2020 | 40 | 2020 |
| Reading comprehension neural networks KM Hermann, T Kocisky, ET Grefenstette, L Espeholt, WT Kay, ... US Patent 10,628,735, 2020 | 23 | 2020 |
| Processing text sequences using neural networks NE Kalchbrenner, K Simonyan, L Espeholt US Patent 10,354,015, 2019 | 23 | 2019 |
| Boosting search engines with interactive agents L Adolphs, B Boerschinger, C Buck, MC Huebscher, M Ciaramita, ... arXiv preprint arXiv:2109.00527, 2021 | 21 | 2021 |
| Speech recognition using convolutional neural networks AGA van den Oord, SEL Dieleman, NE Kalchbrenner, K Simonyan, ... US Patent 10,586,531, 2020 | 15 | 2020 |
| kavukcuoglu, k., Vinyals, O., and Graves, A.(2016). Conditional image generation with PixelCNN decoders A van den Oord, N Kalchbrenner, L Espeholt Lee, DD; Sugiyama, M.; Luxburg, UV; Guyon, I, 4790-4798, 0 | 9 | |
| Skillful twelve hour precipitation forecasts using large context neural networks. arXiv 2021 L Espeholt, S Agrawal, C Sønderby, M Kumar, J Heek, C Bromberg, ... arXiv preprint arXiv:2111.07470, 0 | 8 | |
| Agent-centric representations for multi-agent reinforcement learning W Shang, L Espeholt, A Raichuk, T Salimans arXiv preprint arXiv:2104.09402, 2021 | 7 | 2021 |
| Method for modeling source code having code segments that lack source location J Van Gogh, SF Yegge, MJ Fromberger, A Shali, GS West, JA Dennett, ... US Patent 9,116,780, 2015 | 7 | 2015 |
Nal KalchbrennerGoogle DeepMindDirección de correo verificada de google.com
