Tom Le Paine
Tom Le Paine
Research Scientist, DeepMind
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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
6602019
Alphastar: Mastering the real-time strategy game starcraft ii
O Vinyals, I Babuschkin, J Chung, M Mathieu, M Jaderberg, ...
DeepMind blog 2, 2019
2882019
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
2142015
Seq-nms for video object detection
W Han*, P Khorrami*, TL Paine*, P Ramachandran, M Babaeizadeh, ...
arXiv preprint arXiv:1602.08465, 2016
1572016
Playing hard exploration games by watching youtube
Y Aytar, T Pfaff, D Budden, TL Paine, Z Wang, N de Freitas
arXiv preprint arXiv:1805.11592, 2018
1462018
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
1252010
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
902013
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
852016
Fast wavenet generation algorithm
TL Paine, P Khorrami, S Chang, Y Zhang, P Ramachandran, ...
arXiv preprint arXiv:1611.09482, 2016
652016
Fast generation for convolutional autoregressive models
P Ramachandran*, TL Paine*, P Khorrami, M Babaeizadeh, S Chang, ...
arXiv preprint arXiv:1704.06001, 2017
582017
Large-scale visual speech recognition
B Shillingford, Y Assael, MW Hoffman, T Paine, C Hughes, U Prabhu, ...
arXiv preprint arXiv:1807.05162, 2018
572018
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
532017
An analysis of unsupervised pre-training in light of recent advances
TL Paine*, P Khorrami*, W Han, TS Huang
arXiv preprint arXiv:1412.6597, 2014
502014
Acme: A research framework for distributed reinforcement learning
M Hoffman, B Shahriari, J Aslanides, G Barth-Maron, F Behbahani, ...
arXiv preprint arXiv:2006.00979, 2020
172020
Simultaneous dynamic and functional MRI scanning (SimulScan) of natural swallows
TL Paine, CA Conway, GA Malandraki, BP Sutton
Magnetic resonance in medicine 65 (5), 1247-1252, 2011
172011
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
152019
Image classification using images with separate grayscale and color channels
H Jin, T Le Paine, J Yang, Z Lin, JW Brandt
US Patent 9,230,192, 2016
132016
One-shot high-fidelity imitation: Training large-scale deep nets with rl
TL Paine*, SG Colmenarejo*, Z Wang, S Reed, Y Aytar, T Pfaff, ...
arXiv preprint arXiv:1810.05017, 2018
112018
Oord
S Reed, Y Chen, T Paine
A. vd, 2017
112017
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
102020
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Artiklar 1–20