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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 (01), 5885-5892, 2019
515*2019
Infogail: Interpretable imitation learning from visual demonstrations
Y Li, J Song, S Ermon
Advances in Neural Information Processing Systems 30, 2017
350*2017
Towards deeper understanding of variational autoencoding models
S Zhao, J Song, S Ermon
arXiv preprint arXiv:1702.08658, 2017
1342017
Denoising diffusion implicit models
J Song, C Meng, S Ermon
International Conference on Learning Representations (ICLR) 2021, 2020
1282020
Multi-agent generative adversarial imitation learning
J Song, H Ren, D Sadigh, S Ermon
Neural Information Processing Systems (NeurIPS) 2018, 2018
1272018
Learning hierarchical features from deep generative models
S Zhao, J Song, S Ermon
International Conference on Machine Learning, 4091-4099, 2017
120*2017
Learning controllable fair representations
J Song, P Kalluri, A Grover, S Zhao, S Ermon
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
1122019
Max-margin nonparametric latent feature models for link prediction
J Zhu, J Song, B Chen
arXiv preprint arXiv:1602.07428, 2016
1072016
Understanding the limitations of variational mutual information estimators
J Song, S Ermon
International Conference on Learning Representations (ICLR) 2020, 2019
1052019
A-nice-mc: Adversarial training for mcmc
J Song, S Zhao, S Ermon
Neural Information Processing Systems (NeurIPS) 2017, 2017
912017
Bias correction of learned generative models using likelihood-free importance weighting
A Grover, J Song, A Agarwal, K Tran, A Kapoor, E Horvitz, S Ermon
Neural Information Processing Systems (NeurIPS) 2019, 2019
842019
Bias and generalization in deep generative models: An empirical study
S Zhao, H Ren, A Yuan, J Song, N Goodman, S Ermon
Neural Information Processing Systems (NeurIPS) 2018, 2018
722018
Multi-agent adversarial inverse reinforcement learning
L Yu, J Song, S Ermon
International Conference on Machine Learning, 7194-7201, 2019
672019
The information autoencoding family: A lagrangian perspective on latent variable generative models
S Zhao, J Song, S Ermon
Conference on Uncertainty in Artificial Intelligence (UAI) 2018, 2018
552018
A theory of usable information under computational constraints
Y Xu, S Zhao, J Song, R Stewart, S Ermon
nternational Conference on Learning Representations (ICLR) 2020, 2020
432020
Calibrated model-based deep reinforcement learning
A Malik, V Kuleshov, J Song, D Nemer, H Seymour, S Ermon
International Conference on Machine Learning, 4314-4323, 2019
392019
Negative data augmentation
A Sinha, K Ayush, J Song, B Uzkent, H Jin, S Ermon
International Conference on Learning Representations (ICLR) 2021, 2021
342021
Permutation invariant graph generation via score-based generative modeling
C Niu, Y Song, J Song, S Zhao, A Grover, S Ermon
International Conference on Artificial Intelligence and Statistics, 4474-4484, 2020
342020
Bridging the gap between f-gans and wasserstein gans
J Song, S Ermon
International Conference on Machine Learning, 9078-9087, 2020
302020
Sdedit: Image synthesis and editing with stochastic differential equations
C Meng, Y Song, J Song, J Wu, JY Zhu, S Ermon
arXiv preprint arXiv:2108.01073, 2021
282021
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