Philip Bachman
Philip Bachman
Microsoft Research
Verified email at microsoft.com
Title
Cited by
Cited by
Year
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
11002018
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
10062018
Learning representations by maximizing mutual information across views
P Bachman, RD Hjelm, W Buchwalter
arXiv preprint arXiv:1906.00910, 2019
5112019
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
4992016
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
2832018
Learning with pseudo-ensembles
P Bachman, O Alsharif, D Precup
Advances in neural information processing systems 27, 3365-3373, 2014
2552014
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
1282017
Learning algorithms for active learning
P Bachman, A Sordoni, A Trischler
international conference on machine learning, 301-310, 2017
1212017
Iterative alternating neural attention for machine reading
A Sordoni, P Bachman, A Trischler, Y Bengio
arXiv preprint arXiv:1606.02245, 2016
1172016
Natural language comprehension with the epireader
A Trischler, Z Ye, X Yuan, K Suleman
arXiv preprint arXiv:1606.02270, 2016
942016
Calibrating energy-based generative adversarial networks
Z Dai, A Almahairi, P Bachman, E Hovy, A Courville
arXiv preprint arXiv:1702.01691, 2017
882017
An architecture for deep, hierarchical generative models
P Bachman
arXiv preprint arXiv:1612.04739, 2016
502016
Data generation as sequential decision making
P Bachman, D Precup
Advances in Neural Information Processing Systems 28, 3249-3257, 2015
482015
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
33*2020
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
262016
Deep reinforcement and infomax learning
B Mazoure, RT Combes, T Doan, P Bachman, RD Hjelm
arXiv preprint arXiv:2006.07217, 2020
172020
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
ICML, 1964-1972, 2015
142015
Structure discovery in PPI networks using pattern-based network decomposition
P Bachman, Y Liu
Bioinformatics 25 (14), 1814-1821, 2009
142009
Representation learning with video deep infomax
RD Hjelm, P Bachman
arXiv preprint arXiv:2007.13278, 2020
10*2020
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Articles 1–20