Stochastic Gradient Geodesic MCMC Methods
Jun Zhu , Yang Song , Chang Liu
neural information processing systems 29 3009 -3017
18
2016
Constructing Unrestricted Adversarial Examples with Generative Models
Stefano Ermon , Yang Song , Nate Kushman , Rui Shu
arXiv: Learning
225
2018
Sliced Score Matching: A Scalable Approach to Density and Score Estimation
Stefano Ermon , Yang Song , Jiaxin Shi , Sahaj Garg
arXiv: Learning
133
2019
Generative Modeling by Estimating Gradients of the Data Distribution
Stefano Ermon , Yang Song
arXiv: Learning
730
2019
MintNet: Building Invertible Neural Networks with Masked Convolutions
Stefano Ermon , Yang Song , Chenlin Meng
arXiv: Learning
53
2019
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
Stefano Ermon , Sebastian Nowozin , Taesup Kim , Yang Song
international conference on learning representations
694
2017
Accelerating Natural Gradient with Higher-Order Invariance
Stefano Ermon , Jiaming Song , Yang Song
international conference on machine learning 4713 -4722
12
2018
Training deep neural networks via direct loss minimization
Alexander G. Schwing , Richard S. Zemel , Raquel Urtasun , Yang Song
international conference on machine learning 2169 -2177
57
2016
Efficient Graph Generation with Graph Recurrent Attention Networks
David Duvenaud , Renjie Liao , Yujia Li , Shenlong Wang
arXiv: Learning
215
2019
Unsupervised Out-of-Distribution Detection with Batch Normalization.
Stefano Ermon , Jiaming Song , Yang Song
arXiv: Learning
14
2019
Towards Certified Defense for Unrestricted Adversarial Attacks
Stefano Ermon , Shengjia Zhao , Yang Song
2019
Training Deep Energy-Based Models with f-Divergence Minimization
Stefano Ermon , Lantao Yu , Jiaming Song , Yang Song
arXiv: Learning
28
2020
Improved Techniques for Training Score-Based Generative Models
Stefano Ermon , Yang Song
arXiv: Learning
260
2020
Permutation Invariant Graph Generation via Score-Based Generative Modeling
Stefano Ermon , Aditya Grover , Shengjia Zhao , Jiaming Song
international conference on artificial intelligence and statistics 4474 -4484
65
2020
Imitation with Neural Density Models
Stefano Ermon , Yanan Sui , Jiaming Song , Yang Song
arXiv: Learning
7
2021
Autoregressive Score Matching
Stefano Ermon , Lantao Yu , Jiaming Song , Yang Song
neural information processing systems 33 6673 -6683
6
2020
Efficient Learning of Generative Models via Finite-Difference Score Matching.
Stefano Ermon , Kun Xu , Jun Zhu , Chongxuan Li
neural information processing systems 33 19175 -19188
6
2020
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks.
Stefano Ermon , Yang Song , Yusuke Tashiro
neural information processing systems 33 4536 -4548
44
2020
Score-Based Generative Modeling through Stochastic Differential Equations
Stefano Ermon , Diederik P. Kingma , Ben Poole , Abhishek Kumar
arXiv: Learning
692
2020
Learning Energy-Based Models by Diffusion Recovery Likelihood
Diederik P Kingma , Ying Nian Wu , Ben Poole , Yang Song
international conference on learning representations
47
2021