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
7842018
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
5362018
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
3702016
Learning representations by maximizing mutual information across views
P Bachman, RD Hjelm, W Buchwalter
arXiv preprint arXiv:1906.00910, 2019
2562019
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
2132018
Learning with pseudo-ensembles
P Bachman, O Alsharif, D Precup
arXiv preprint arXiv:1412.4864, 2014
1732014
Iterative alternating neural attention for machine reading
A Sordoni, P Bachman, A Trischler, Y Bengio
arXiv preprint arXiv:1606.02245, 2016
1052016
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
1042017
Learning algorithms for active learning
P Bachman, A Sordoni, A Trischler
international conference on machine learning, 301-310, 2017
882017
Natural language comprehension with the epireader
A Trischler, Z Ye, X Yuan, K Suleman
arXiv preprint arXiv:1606.02270, 2016
862016
Calibrating energy-based generative adversarial networks
Z Dai, A Almahairi, P Bachman, E Hovy, A Courville
arXiv preprint arXiv:1702.01691, 2017
762017
An architecture for deep, hierarchical generative models
P Bachman
arXiv preprint arXiv:1612.04739, 2016
452016
Data generation as sequential decision making
P Bachman, D Precup
arXiv preprint arXiv:1506.03504, 2015
412015
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
212016
Training deep generative models: Variations on a theme
P Bachman, D Precup
NIPS Approximate Inference Workshop, 2015
122015
Structure discovery in PPI networks using pattern-based network decomposition
P Bachman, Y Liu
Bioinformatics 25 (14), 1814-1821, 2009
122009
Variational Generative Stochastic Networks with Collaborative Shaping.
P Bachman, D Precup
ICML, 1964-1972, 2015
112015
Towards information-seeking agents
P Bachman, A Sordoni, A Trischler
arXiv preprint arXiv:1612.02605, 2016
82016
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
62020
Vfunc: a deep generative model for functions
P Bachman, R Islam, A Sordoni, Z Ahmed
arXiv preprint arXiv:1807.04106, 2018
62018
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Articles 1–20