Andrew Jaegle
Andrew Jaegle
Staff Research Scientist, Google DeepMind
Verified email at - Homepage
Cited by
Cited by
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
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
Hamiltonian generative networks
P Toth, DJ Rezende, A Jaegle, S Racanière, A Botev, I Higgins
International Conference on Learning Representations (ICLR), 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
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
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
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
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
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
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
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
Fast, robust, continuous monocular egomotion computation
A Jaegle, S Phillips, K Daniilidis
2016 IEEE International Conference on Robotics and Automation (ICRA), 773-780, 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
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
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
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
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
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
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
Imitation by Predicting Observations
A Jaegle, Y Sulsky, A Ahuja, J Bruce, R Fergus, G Wayne
International Conference on Machine Learning (ICML), 4665-4676, 2021
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