| 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 | 1490 | 2023 |
| Vector-based navigation using grid-like representations in artificial agents A Banino, C Barry, B Uria, C Blundell, T Lillicrap, P Mirowski, A Pritzel, ... Nature 557 (7705), 429-433, 2018 | 736 | 2018 |
| Neural episodic control A Pritzel, B Uria, S Srinivasan, AP Badia, O Vinyals, D Hassabis, ... International conference on machine learning, 2827-2836, 2017 | 423 | 2017 |
| Neural autoregressive distribution estimation B Uria, MA Côté, K Gregor, I Murray, H Larochelle Journal of Machine Learning Research 17 (205), 1-37, 2016 | 396 | 2016 |
| Model-free episodic control C Blundell, B Uria, A Pritzel, Y Li, A Ruderman, JZ Leibo, J Rae, ... arXiv preprint arXiv:1606.04460, 2016 | 274 | 2016 |
| RNADE: The real-valued neural autoregressive density-estimator B Uria, I Murray, H Larochelle Advances in Neural Information Processing Systems 26, 2013 | 270 | 2013 |
| Associative long short-term memory I Danihelka, G Wayne, B Uria, N Kalchbrenner, A Graves International conference on machine learning, 1986-1994, 2016 | 204 | 2016 |
| Early visual concept learning with unsupervised deep learning I Higgins, L Matthey, X Glorot, A Pal, B Uria, C Blundell, S Mohamed, ... arXiv preprint arXiv:1606.05579, 2016 | 198 | 2016 |
| A deep and tractable density estimator B Uria, I Murray, H Larochelle Proceedings of the 31th International Conference on Machine Learning, ICML …, 2013 | 188 | 2013 |
| Memory-based parameter adaptation P Sprechmann, SM Jayakumar, JW Rae, A Pritzel, AP Badia, B Uria, ... arXiv preprint arXiv:1802.10542, 2018 | 114 | 2018 |
| Deep architectures for articulatory inversion B Uria, I Murray, S Renals, K Richmond INTERSPEECH 2012 13th Annual Conference of the International Speech …, 2012 | 86 | 2012 |
| 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 |
| A deep neural network for acoustic-articulatory speech inversion B Uria, S Renals, K Richmond Proc. NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning, 2011 | 63 | 2011 |
| Modelling acoustic feature dependencies with artificial neural networks: Trajectory-RNADE B Uria, I Murray, S Renals, C Valentini, J Bridle Proceedings of the IEEE International Conference on Acoustics, Speech, and …, 2015 | 38 | 2015 |
| The spatial memory pipeline: a model of egocentric to allocentric understanding in mammalian brains B Uria, B Ibarz, A Banino, V Zambaldi, D Kumaran, D Hassabis, C Barry, ... BioRxiv, 2020.11. 11.378141, 2020 | 35 | 2020 |
| Model-free episodic control. arXiv preprint 1606.04460 C Blundell, B Uria, A Pritzel, Y Li, A Ruderman, JZ Leibo, J Rae, ... | 29 | 2016 |
| On the evaluation of inversion mapping performance in the acoustic domain K Richmond, Z Ling, J Yamagishi Proc. Interspeech, 1012-1016, 2013 | 9 | 2013 |
| Connectionist multivariate density-estimation and its application to speech synthesis B Uria The University of Edinburgh, 2015 | 3 | 2015 |
| Vector-based navigation using grid-like representations in artificial agents A Pritzel, A Banino, B Uria, BC Zhang, C Barry, C Blundell, C Beattie, ... | 2 | 2018 |
| Imagen 3 J Baldridge, J Bauer, M Bhutani, N Brichtova, A Bunner, K Chan, Y Chen, ... arXiv preprint arXiv:2408.07009, 2024 | 1 | 2024 |
Iain MurraySchool of Informatics, University of EdinburghVerified email at ed.ac.uk
