Yang Song (宋飏)
Yang Song (宋飏)
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
5292018
Generative modeling by estimating gradients of the data distribution
Y Song, S Ermon
Advances in Neural Information Processing Systems, 11918-11930, 2019
2282019
Constructing Unrestricted Adversarial Examples with Generative Models
Y Song, R Shu, N Kushman, S Ermon
Advances in Neural Information Processing Systems, 8322-8333, 2018
1822018
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
1232019
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
1152021
Improved techniques for training score-based generative models
Y Song, S Ermon
Advances in Neural Information Processing Systems 33, 2020
882020
Training deep neural networks via direct loss minimization
Y Song, A Schwing, R Zemel, R Urtasun
International Conference on Machine Learning, 2169-2177, 2016
86*2016
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
582019
Mintnet: Building invertible neural networks with masked convolutions
Y Song, C Meng, S Ermon
Advances in Neural Information Processing Systems, 11004-11014, 2019
382019
How to Train Your Energy-Based Models
Y Song, DP Kingma
arXiv preprint arXiv:2101.03288, 2021
312021
Stochastic gradient geodesic mcmc methods
C Liu, J Zhu, Y Song
Advances in neural information processing systems 29, 3009-3017, 2016
272016
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
24*2020
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
232020
Learning Energy-Based Models by Diffusion Recovery Likelihood
R Gao, Y Song, B Poole, YN Wu, DP Kingma
International Conference on Learning Representations, 2020
202020
Training deep energy-based models with f-divergence minimization
L Yu, Y Song, J Song, S Ermon
International Conference on Machine Learning, 10957-10967, 2020
192020
Gaussianization flows
C Meng, Y Song, J Song, S Ermon
International Conference on Artificial Intelligence and Statistics, 4336-4345, 2020
162020
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
112021
Unsupervised Out-of-Distribution Detection with Batch Normalization
J Song, Y Song, S Ermon
arXiv preprint arXiv:1910.09115, 2019
112019
Accelerating Natural Gradient with Higher-Order Invariance
Y Song, J Song, S Ermon
International Conference on Machine Learning, 2018
102018
Maximum Likelihood Training of Score-Based Diffusion Models
Y Song, C Durkan, I Murray, S Ermon
arXiv preprint arXiv:2101.09258, 2021
9*2021
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Articles 1–20