Tom Le Paine
Tom Le Paine
Staff Research Scientist at Google DeepMind
Verified email at - Homepage
Cited by
Cited by
Grandmaster level in StarCraft II using multi-agent reinforcement learning
O Vinyals, I Babuschkin, WM Czarnecki, M Mathieu, A Dudzik, J Chung, ...
nature 575 (7782), 350-354, 2019
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
Alphastar: Mastering the real-time strategy game starcraft ii
O Vinyals, I Babuschkin, J Chung, M Mathieu, M Jaderberg, ...
DeepMind blog 2, 20, 2019
Seq-nms for video object detection
W Han*, P Khorrami*, TL Paine*, P Ramachandran, M Babaeizadeh, ...
arXiv preprint arXiv:1602.08465, 2016
Do deep neural networks learn facial action units when doing expression recognition?
P Khorrami, T Paine, T Huang
Proceedings of the IEEE international conference on computer vision …, 2015
Playing hard exploration games by watching youtube
Y Aytar, T Pfaff, D Budden, T Paine, Z Wang, N De Freitas
Advances in neural information processing systems 31, 2018
Acme: A research framework for distributed reinforcement learning
MW Hoffman, B Shahriari, J Aslanides, G Barth-Maron, N Momchev, ...
arXiv preprint arXiv:2006.00979, 2020
Large-scale visual speech recognition
B Shillingford, Y Assael, MW Hoffman, T Paine, C Hughes, U Prabhu, ...
arXiv preprint arXiv:1807.05162, 2018
Rl unplugged: Benchmarks for offline reinforcement learning
C Gulcehre*, Z Wang*, A Novikov*, TL Paine*, SG Colmenarejo, K Zolna, ...
arXiv preprint arXiv:2006.13888, 2020
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ...
arXiv preprint arXiv:2403.05530, 2024
Optimized preload leakage-correction methods to improve the diagnostic accuracy of dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in posttreatment gliomas
LS Hu, LC Baxter, DS Pinnaduwage, TL Paine, JP Karis, BG Feuerstein, ...
American Journal of Neuroradiology 31 (1), 40-48, 2010
How deep neural networks can improve emotion recognition on video data
P Khorrami, T Le Paine, K Brady, C Dagli, TS Huang
2016 IEEE international conference on image processing (ICIP), 619-623, 2016
Hyperparameter selection for offline reinforcement learning
TL Paine*, C Paduraru*, A Michi, C Gulcehre, K Zolna, A Novikov, Z Wang, ...
arXiv preprint arXiv:2007.09055, 2020
GPU asynchronous stochastic gradient descent to speed up neural network training
TL Paine, H Jin, J Yang, Z Lin, T Huang
arXiv preprint arXiv:1312.6186, 2013
Fast wavenet generation algorithm
TL Paine, P Khorrami, S Chang, Y Zhang, P Ramachandran, ...
arXiv preprint arXiv:1611.09482, 2016
Reinforced self-training (rest) for language modeling
C Gulcehre*, TL Paine*, S Srinivasan*, K Konyushkova, L Weerts, ...
arXiv preprint arXiv:2308.08998, 2023
Few-shot autoregressive density estimation: Towards learning to learn distributions
S Reed, Y Chen, T Paine, A Oord, SM Eslami, D Rezende, O Vinyals, ...
arXiv preprint arXiv:1710.10304, 2017
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
TL Paine*, C Gulcehre*, B Shahriari, M Denil, M Hoffman, H Soyer, ...
arXiv preprint arXiv:1909.01387, 2019
Fast generation for convolutional autoregressive models
P Ramachandran*, TL Paine*, P Khorrami, M Babaeizadeh, S Chang, ...
arXiv preprint arXiv:1704.06001, 2017
Benchmarks for deep off-policy evaluation
J Fu, M Norouzi, O Nachum, G Tucker, Z Wang, A Novikov, M Yang, ...
arXiv preprint arXiv:2103.16596, 2021
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