| Perceiver: General perception with iterative attention A Jaegle, F Gimeno, A Brock, A Zisserman, O Vinyals, J Carreira International Conference on Machine Learning (ICML), 2021 | 917 | 2021 |
| Perceiver IO: A general architecture for structured inputs & outputs A Jaegle, S Borgeaud, JB Alayrac, C Doersch, C Ionescu, D Ding, ... arXiv preprint arXiv:2107.14795, 2021 | 552 | 2021 |
| Hamiltonian generative networks P Toth, DJ Rezende, A Jaegle, S Racanière, A Botev, I Higgins International Conference on Learning Representations (ICLR), 2020 | 237 | 2020 |
| Game Plan: What AI can do for Football, and What Football can do for AI K Tuyls, S Omidshafiei, P Muller, Z Wang, J Connor, D Hennes, I Graham, ... Journal of Artificial Intelligence Research 71, 41-88, 2021 | 101 | 2021 |
| Object discovery and representation networks OJ Hénaff, S Koppula, E Shelhamer, D Zoran, A Jaegle, A Zisserman, ... European conference on computer vision, 123-143, 2022 | 87 | 2022 |
| Towards learning universal audio representations L Wang, P Luc, Y Wu, A Recasens, L Smaira, A Brock, A Jaegle, ... ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 74 | 2022 |
| Emergence of invariant representation of vocalizations in the auditory cortex IM Carruthers, DA Laplagne, A Jaegle, JJ Briguglio, ... Journal of neurophysiology 114 (5), 2726-2740, 2015 | 72 | 2015 |
| Population response magnitude variation in inferotemporal cortex predicts image memorability A Jaegle, V Mehrpour, Y Mohsenzadeh, T Meyer, A Oliva, N Rust Elife 8, e47596, 2019 | 69 | 2019 |
| Visual novelty, curiosity, and intrinsic reward in machine learning and the brain A Jaegle, V Mehrpour, N Rust Current opinion in neurobiology 58, 167-174, 2019 | 68 | 2019 |
| General-purpose, long-context autoregressive modeling with Perceiver AR C Hawthorne, A Jaegle, C Cangea, S Borgeaud, C Nash, M Malinowski, ... International Conference on Machine Learning, 8535-8558, 2022 | 62 | 2022 |
| Direct control of visual perception with phase-specific modulation of posterior parietal cortex A Jaegle, T Ro Journal of cognitive neuroscience 26 (2), 422-432, 2014 | 62 | 2014 |
| Fast, robust, continuous monocular egomotion computation A Jaegle, S Phillips, K Daniilidis 2016 IEEE International Conference on Robotics and Automation (ICRA), 773-780, 2016 | 39 | 2016 |
| Transframer: Arbitrary frame prediction with generative models C Nash, J Carreira, J Walker, I Barr, A Jaegle, M Malinowski, P Battaglia arXiv preprint arXiv:2203.09494, 2022 | 35 | 2022 |
| Keyframing the Future: Keyframe Discovery for Visual Prediction and Planning K Pertsch, O Rybkin, J Yang, KG Derpanis, K Daniilidis, J Lim, A Jaegle Learning for Dynamics & Control (L4DC), 2020 | 31* | 2020 |
| Learning what you can do before doing anything O Rybkin, K Pertsch, KG Derpanis, K Daniilidis, A Jaegle International Conference on Learning Representations (ICLR), 2019 | 31* | 2019 |
| Which priors matter? Benchmarking models for learning latent dynamics A Botev, A Jaegle, P Wirnsberger, D Hennes, I Higgins Neural Information Processing Systems (NeurIPS), 2021 | 26 | 2021 |
| Zorro: the masked multimodal transformer A Recasens, J Lin, J Carreira, D Jaegle, L Wang, J Alayrac, P Luc, ... arXiv preprint arXiv:2301.09595, 2023 | 20 | 2023 |
| Physically embedded planning problems: New challenges for reinforcement learning M Mirza, A Jaegle, JJ Hunt, A Guez, S Tunyasuvunakool, A Muldal, ... arXiv preprint arXiv:2009.05524, 2020 | 12 | 2020 |
| Where should i spend my flops? efficiency evaluations of visual pre-training methods S Koppula, Y Li, E Shelhamer, A Jaegle, N Parthasarathy, R Arandjelovic, ... arXiv preprint arXiv:2209.15589, 2022 | 11 | 2022 |
| Soda: Bottleneck diffusion models for representation learning DA Hudson, D Zoran, M Malinowski, AK Lampinen, A Jaegle, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 8 | 2024 |
João CarreiraGoogle DeepMindVerified email at google.com
