Yang Song (宋飏)
Yang Song (宋飏)
在 cs.stanford.edu 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
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
4282018
Generative modeling by estimating gradients of the data distribution
Y Song, S Ermon
Advances in Neural Information Processing Systems, 11918-11930, 2019
1482019
Constructing Unrestricted Adversarial Examples with Generative Models
Y Song, R Shu, N Kushman, S Ermon
Advances in Neural Information Processing Systems, 8322-8333, 2018
138*2018
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
762019
Training deep neural networks via direct loss minimization
Y Song, A Schwing, R Zemel, R Urtasun
International Conference on Machine Learning, 2169-2177, 2016
70*2016
Improved techniques for training score-based generative models
Y Song, S Ermon
Advances in Neural Information Processing Systems 33, 2020
472020
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
462019
Score-Based Generative Modeling through Stochastic Differential Equations
Y Song, J Sohl-Dickstein, DP Kingma, A Kumar, S Ermon, B Poole
arXiv preprint arXiv:2011.13456, 2020
422020
Mintnet: Building invertible neural networks with masked convolutions
Y Song, C Meng, S Ermon
Advances in Neural Information Processing Systems, 11004-11014, 2019
282019
Stochastic gradient geodesic mcmc methods
C Liu, J Zhu, Y Song
Advances in neural information processing systems 29, 3009-3017, 2016
262016
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
152020
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
122020
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
11*2020
Learning Energy-Based Models by Diffusion Recovery Likelihood
R Gao, Y Song, B Poole, YN Wu, DP Kingma
arXiv preprint arXiv:2012.08125, 2020
102020
Gaussianization flows
C Meng, Y Song, J Song, S Ermon
International Conference on Artificial Intelligence and Statistics, 4336-4345, 2020
102020
Unsupervised Out-of-Distribution Detection with Batch Normalization
J Song, Y Song, S Ermon
arXiv preprint arXiv:1910.09115, 2019
102019
Bayesian matrix completion via adaptive relaxed spectral regularization
Y Song, J Zhu
Thirtieth AAAI Conference on Artificial Intelligence, 2016
92016
How to Train Your Energy-Based Models
Y Song, DP Kingma
arXiv preprint arXiv:2101.03288, 2021
82021
Kernel Bayesian Inference with posterior regularization
Y Song, J Zhu, Y Ren
Advances in Neural Information Processing Systems 29, 4763-4771, 2016
82016
Accelerating Natural Gradient with Higher-Order Invariance
Y Song, J Song, S Ermon
International Conference on Machine Learning, 2018
72018
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