| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context Gemini Team, Google arXiv preprint arXiv:2403.05530v3, 2024 | 3221* | 2024 |
| Learning to communicate with deep multi-agent reinforcement learning JN Foerster, YM Assael, N de Freitas, S Whiteson Advances in Neural Information Processing Systems, 2145-2153, 2016 | 2592 | 2016 |
| Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities Gemini Team, Google arXiv preprint arXiv:2507.06261, 2025 | 1321 | 2025 |
| Effective gene expression prediction from sequence by integrating long-range interactions Z Avsec, V Agarwal, D Visentin, JR Ledsam, A Grabska-Barwinska, ... Nature Methods 18 (10), 1196-1203, 2021 | 1314 | 2021 |
| LipNet: end-to-end sentence-level lipreading YM Assael, B Shillingford, S Whiteson, N De Freitas GPU Technology Conference, 2017 | 849* | 2017 |
| Restoring and attributing ancient texts using deep neural networks Y Assael, T Sommerschield, B Shillingford, M Bordbar, J Pavlopoulos, ... Nature 603 (7900), 280-283, 2022 | 320 | 2022 |
| Multi-objective deep reinforcement learning H Mossalam, YM Assael, DM Roijers, S Whiteson Advances in Neural Information Processing Systems Deep Reinforcement …, 2016 | 251 | 2016 |
| Large-scale visual speech recognition B Shillingford, Y Assael, MW Hoffman, T Paine, C Hughes, U Prabhu, ... INTERSPEECH, 4135-4139, 2019 | 244 | 2019 |
| Recurrent neural network transducer for audio-visual speech recognition T Makino, H Liao, Y Assael, B Shillingford, B Garcia, O Braga, O Siohan IEEE Automatic Speech Recognition and Understanding Workshop, 2019 | 214 | 2019 |
| Sample efficient adaptive text-to-speech Y Chen, Y Assael, B Shillingford, D Budden, S Reed, H Zen, Q Wang, ... International Conference on Learning Representations, 2019 | 213* | 2019 |
| Learning to communicate to solve riddles with deep distributed recurrent Q-networks JN Foerster, YM Assael, N de Freitas, S Whiteson International Joint Conferences on Artificial Intelligence Workshop, 2016 | 207* | 2016 |
| Evaluating frontier models for dangerous capabilities M Phuong, M Aitchison, E Catt, S Cogan, A Kaskasoli, V Krakovna, ... arXiv preprint arXiv:2403.13793, 2024 | 148* | 2024 |
| Restoring ancient text using deep learning: a case study on Greek epigraphy Y Assael, T Sommerschield, J Prag Empirical Methods in Natural Language Processing, 6369-6376, 2019 | 133 | 2019 |
| Machine learning for ancient languages: a survey T Sommerschield, Y Assael, J Pavlopoulos, V Stefanak, A Senior, C Dyer, ... Computational Linguistics 49 (2), 1-44, 2023 | 130 | 2023 |
| Correlation of the thermal conductivity of normal and parahydrogen from the triple point to 1000 K and up to 100 MPa MJ Assael, YM Assael, ML Huber, RA Perkins, Y Takata Journal of Physical and Chemical Reference Data 40 (3), 033101-033101-13, 2011 | 102 | 2011 |
| Cortical microcircuits as gated-recurrent neural networks R Costa, IA Assael, B Shillingford, N De Freitas, T Vogels Advances in neural information processing systems 30, 2017 | 86 | 2017 |
| Speech bandwidth extension with WaveNet A Gupta, B Shillingford, Y Assael, TC Walters IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2019 | 47 | 2019 |
| Using deep Q-learning to understand the tax evasion behavior of risk-averse firms ND Goumagias, D Hristu-Varsakelis, YM Assael Expert Systems with Applications 101, 258-270, 2018 | 45 | 2018 |
| A novel portable absolute transient hot-wire instrument for the measurement of the thermal conductivity of solids MJ Assael, KD Antoniadis, IN Metaxa, SK Mylona, JAM Assael, J Wu, ... International Journal of Thermophysics 36 (10-11), 3083-3105, 2015 | 39 | 2015 |
| LearnLM: Improving Gemini for learning A Modi, AS Veerubhotla, A Rysbek, A Huber, B Wiltshire, B Veprek, ... arXiv preprint arXiv:2412.16429, 2024 | 34* | 2024 |
Brendan ShillingfordDeepMindVerified email at cs.ox.ac.uk
