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Philip Bachman
Philip Bachman
Microsoft Research
Verified email at microsoft.com
Title
Cited by
Cited by
Year
Learning deep representations by mutual information estimation and maximization
RD Hjelm, A Fedorov, S Lavoie-Marchildon, K Grewal, P Bachman, ...
arXiv preprint arXiv:1808.06670, 2018
15412018
Deep reinforcement learning that matters
P Henderson, R Islam, P Bachman, J Pineau, D Precup, D Meger
Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018
14742018
Learning representations by maximizing mutual information across views
P Bachman, RD Hjelm, W Buchwalter
Advances in neural information processing systems 32, 2019
8062019
Newsqa: A machine comprehension dataset
A Trischler, T Wang, X Yuan, J Harris, A Sordoni, P Bachman, K Suleman
arXiv preprint arXiv:1611.09830, 2016
6102016
Augmented cyclegan: Learning many-to-many mappings from unpaired data
A Almahairi, S Rajeshwar, A Sordoni, P Bachman, A Courville
International Conference on Machine Learning, 195-204, 2018
3592018
Learning with pseudo-ensembles
P Bachman, O Alsharif, D Precup
Advances in neural information processing systems 27, 2014
3562014
Machine comprehension by text-to-text neural question generation
X Yuan, T Wang, C Gulcehre, A Sordoni, P Bachman, S Subramanian, ...
arXiv preprint arXiv:1705.02012, 2017
1472017
Learning algorithms for active learning
P Bachman, A Sordoni, A Trischler
international conference on machine learning, 301-310, 2017
1462017
Iterative alternating neural attention for machine reading
A Sordoni, P Bachman, A Trischler, Y Bengio
arXiv preprint arXiv:1606.02245, 2016
1232016
Calibrating energy-based generative adversarial networks
Z Dai, A Almahairi, P Bachman, E Hovy, A Courville
arXiv preprint arXiv:1702.01691, 2017
972017
Natural language comprehension with the epireader
A Trischler, Z Ye, X Yuan, K Suleman
arXiv preprint arXiv:1606.02270, 2016
942016
Data-efficient reinforcement learning with self-predictive representations
M Schwarzer, A Anand, R Goel, RD Hjelm, A Courville, P Bachman
arXiv preprint arXiv:2007.05929, 2020
892020
Data generation as sequential decision making
P Bachman, D Precup
Advances in Neural Information Processing Systems 28, 2015
572015
An architecture for deep, hierarchical generative models
P Bachman
Advances in Neural Information Processing Systems 29, 2016
542016
Deep reinforcement and infomax learning
B Mazoure, R Tachet des Combes, TL Doan, P Bachman, RD Hjelm
Advances in Neural Information Processing Systems 33, 3686-3698, 2020
392020
Natural language generation in dialogue using lexicalized and delexicalized data
S Sharma, J He, K Suleman, H Schulz, P Bachman
arXiv preprint arXiv:1606.03632, 2016
272016
Pretraining representations for data-efficient reinforcement learning
M Schwarzer, N Rajkumar, M Noukhovitch, A Anand, L Charlin, RD Hjelm, ...
Advances in Neural Information Processing Systems 34, 12686-12699, 2021
18*2021
Representation learning with video deep infomax
RD Hjelm, P Bachman
arXiv preprint arXiv:2007.13278, 2020
18*2020
Training deep generative models: Variations on a theme
P Bachman, D Precup
NIPS Approximate Inference Workshop, 2015
162015
Variational generative stochastic networks with collaborative shaping
P Bachman, D Precup
arXiv preprint arXiv:1708.00805, 2017
142017
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