| 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 | 4058 | 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 | 2840 | 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 | 1661 | 2018 |
| Neural machine translation in linear time N Kalchbrenner arXiv preprint arXiv:1610.10099, 2016 | 704 | 2016 |
| 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 | 387 | 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 | 321 | 2020 |
| 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 | 315 | 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 | 202 | 2022 |
| 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 | 146 | 2019 |
| Processing sequences using convolutional neural networks AGA van den Oord, SEL Dieleman, NE Kalchbrenner, K Simonyan, ... US Patent 11,080,591, 2021 | 84 | 2021 |
| k. kavukcuoglu, O A van den Oord, N Kalchbrenner, L Espeholt Neural discrete representation learning,” in Advances in Neural Information …, 2017 | 64* | 2017 |
| Deep learning for day forecasts from sparse observations M Andrychowicz, L Espeholt, D Li, S Merchant, A Merose, F Zyda, ... arXiv preprint arXiv:2306.06079, 2023 | 34 | 2023 |
| Processing text sequences using neural networks NE Kalchbrenner, K Simonyan, L Espeholt US Patent 10,354,015, 2019 | 32 | 2019 |
| Boosting search engines with interactive agents L Adolphs, B Boerschinger, C Buck, MC Huebscher, M Ciaramita, ... arXiv preprint arXiv:2109.00527, 2021 | 25 | 2021 |
| Reading comprehension neural networks KM Hermann, T Kocisky, ET Grefenstette, L Espeholt, WT Kay, ... US Patent 10,628,735, 2020 | 25 | 2020 |
| Speech recognition using convolutional neural networks AGA van den Oord, SEL Dieleman, NE Kalchbrenner, K Simonyan, ... US Patent 10,586,531, 2020 | 17 | 2020 |
| Agent-centric representations for multi-agent reinforcement learning W Shang, L Espeholt, A Raichuk, T Salimans arXiv preprint arXiv:2104.09402, 2021 | 12 | 2021 |
| 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 | 10 | |
| Processing text sequences using neural networks NE Kalchbrenner, K Simonyan, L Espeholt US Patent 11,321,542, 2022 | 8 | 2022 |
| 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 DeepMindVerified email at google.com
