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Yang Song
Yang Song
OpenAI
Verified email at cs.stanford.edu - Homepage
Title
Cited by
Cited by
Year
Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Y Song, T Kim, S Nowozin, S Ermon, N Kushman
International Conference on Learning Representations, 2018
6822018
Generative modeling by estimating gradients of the data distribution
Y Song, S Ermon
Advances in Neural Information Processing Systems, 11918-11930, 2019
6582019
Score-Based Generative Modeling through Stochastic Differential Equations
Y Song, J Sohl-Dickstein, DP Kingma, A Kumar, S Ermon, B Poole
International Conference on Learning Representations, 2021
5902021
Constructing Unrestricted Adversarial Examples with Generative Models
Y Song, R Shu, N Kushman, S Ermon
Advances in Neural Information Processing Systems, 8322-8333, 2018
2352018
Improved techniques for training score-based generative models
Y Song, S Ermon
Advances in Neural Information Processing Systems 33, 2020
2312020
Efficient graph generation with graph recurrent attention networks
R Liao, Y Li, Y Song, S Wang, W Hamilton, DK Duvenaud, R Urtasun, ...
Advances in Neural Information Processing Systems, 4255-4265, 2019
2052019
SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
C Meng, Y He, Y Song, J Song, J Wu, JY Zhu, S Ermon
International Conference on Learning Representations, 2021
124*2021
Sliced score matching: A scalable approach to density and score estimation
Y Song, S Garg, J Shi, S Ermon
Uncertainty in Artificial Intelligence, 574-584, 2019
1232019
Training deep neural networks via direct loss minimization
Y Song, A Schwing, R Zemel, R Urtasun
International Conference on Machine Learning, 2169-2177, 2016
1012016
Maximum Likelihood Training of Score-Based Diffusion Models
Y Song, C Durkan, I Murray, S Ermon
arXiv preprint arXiv:2101.09258, 2021
96*2021
How to Train Your Energy-Based Models
Y Song, DP Kingma
arXiv preprint arXiv:2101.03288, 2021
912021
GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation
M Xu, L Yu, Y Song, C Shi, S Ermon, J Tang
International Conference on Learning Representations, 2021
662021
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
582020
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models
Y Song, L Shen, L Xing, S Ermon
arXiv preprint arXiv:2111.08005, 2021
532021
Diversity can be Transferred: Output Diversification for White-and Black-box Attacks
Y Tashiro, Y Song, S Ermon
Advances in Neural Information Processing Systems 33, 2020
51*2020
Mintnet: Building invertible neural networks with masked convolutions
Y Song, C Meng, S Ermon
Advances in Neural Information Processing Systems, 11004-11014, 2019
502019
Learning Energy-Based Models by Diffusion Recovery Likelihood
R Gao, Y Song, B Poole, YN Wu, DP Kingma
International Conference on Learning Representations, 2020
432020
CSDI: Conditional score-based diffusion models for probabilistic time series imputation
Y Tashiro, J Song, Y Song, S Ermon
Advances in Neural Information Processing Systems 34, 24804-24816, 2021
372021
Diffusion models: A comprehensive survey of methods and applications
L Yang, Z Zhang, Y Song, S Hong, R Xu, Y Zhao, Y Shao, W Zhang, B Cui, ...
arXiv preprint arXiv:2209.00796, 2022
362022
Stochastic gradient geodesic mcmc methods
C Liu, J Zhu, Y Song
Advances in neural information processing systems 29, 3009-3017, 2016
322016
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Articles 1–20