Jiaming Song
Jiaming Song
Verified email at cs.stanford.edu - Homepage
Title
Cited by
Cited by
Year
Infovae: Balancing learning and inference in variational autoencoders
S Zhao, J Song, S Ermon
Proceedings of the aaai conference on artificial intelligence 33, 5885-5892, 2019
290*2019
Infogail: Interpretable imitation learning from visual demonstrations
Y Li, J Song, S Ermon
Advances in Neural Information Processing Systems, 3812-3822, 2017
217*2017
Max-margin nonparametric latent feature models for link prediction
J Zhu, J Song, B Chen
arXiv preprint arXiv:1602.07428, 2016
1012016
Towards deeper understanding of variational autoencoding models
S Zhao, J Song, S Ermon
arXiv preprint arXiv:1702.08658, 2017
832017
Learning hierarchical features from deep generative models
S Zhao, J Song, S Ermon
International Conference on Machine Learning, 4091-4099, 2017
82*2017
A-nice-mc: Adversarial training for mcmc
J Song, S Zhao, S Ermon
Advances in Neural Information Processing Systems, 5140-5150, 2017
522017
Multi-agent generative adversarial imitation learning
J Song, H Ren, D Sadigh, S Ermon
Advances in neural information processing systems, 7461-7472, 2018
512018
Learning controllable fair representations
J Song, P Kalluri, A Grover, S Zhao, S Ermon
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
432019
Bias correction of learned generative models using likelihood-free importance weighting
A Grover, J Song, A Kapoor, K Tran, A Agarwal, EJ Horvitz, S Ermon
Advances in Neural Information Processing Systems, 11058-11070, 2019
302019
Bias and generalization in deep generative models: An empirical study
S Zhao, H Ren, A Yuan, J Song, N Goodman, S Ermon
Advances in Neural Information Processing Systems, 10792-10801, 2018
272018
Understanding the limitations of variational mutual information estimators
J Song, S Ermon
arXiv preprint arXiv:1910.06222, 2019
262019
The information autoencoding family: A lagrangian perspective on latent variable generative models
S Zhao, J Song, S Ermon
arXiv preprint arXiv:1806.06514, 2018
242018
Multi-agent adversarial inverse reinforcement learning
L Yu, J Song, S Ermon
arXiv preprint arXiv:1907.13220, 2019
182019
Calibrated model-based deep reinforcement learning
A Malik, V Kuleshov, J Song, D Nemer, H Seymour, S Ermon
arXiv preprint arXiv:1906.08312, 2019
142019
Learning with weak supervision from physics and data-driven constraints
H Ren, R Stewart, J Song, V Kuleshov, S Ermon
AI Magazine 39 (1), 27-38, 2018
132018
Factored temporal sigmoid belief networks for sequence learning
J Song, Z Gan, L Carin
International Conference on Machine Learning, 1272-1281, 2016
122016
A theory of usable information under computational constraints
Y Xu, S Zhao, J Song, R Stewart, S Ermon
arXiv preprint arXiv:2002.10689, 2020
102020
Adversarial constraint learning for structured prediction
H Ren, R Stewart, J Song, V Kuleshov, S Ermon
arXiv preprint arXiv:1805.10561, 2018
102018
Training Deep Energy-Based Models with f-Divergence Minimization
L Yu, Y Song, J Song, S Ermon
arXiv preprint arXiv:2003.03463, 2020
82020
Unsupervised Out-of-Distribution Detection with Batch Normalization
J Song, Y Song, S Ermon
arXiv preprint arXiv:1910.09115, 2019
82019
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