Yang Song

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 60

Top Topics

Generative Model

Efficient Learning

Stochastic Differential Equations

Top Venues

Publications

Bingliang Zhang, Wenda Chu, Julius Berner, Chenlin Meng, Anima Anandkumar, Yang Song
Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing. CoRR (2024)
Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, Ming-Hsuan Yang
Diffusion Models: A Comprehensive Survey of Methods and Applications. ACM Comput. Surv. 56 (4) (2024)
Yang Song, Liyue Shen, Lei Xing, Stefano Ermon
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models. ICLR (2022)
Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Ming-Hsuan Yang, Bin Cui
Diffusion Models: A Comprehensive Survey of Methods and Applications. CoRR (2022)
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang
GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation. ICLR (2022)
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang
GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation. CoRR (2022)
Kristy Choi, Chenlin Meng, Yang Song, Stefano Ermon
Density Ratio Estimation via Infinitesimal Classification. AISTATS (2022)
Yang Song, Conor Durkan, Iain Murray, Stefano Ermon
Maximum Likelihood Training of Score-Based Diffusion Models. NeurIPS (2021)
Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon
Improved Autoregressive Modeling with Distribution Smoothing. ICLR (2021)
Chenlin Meng, Yang Song, Wenzhe Li, Stefano Ermon
Estimating High Order Gradients of the Data Distribution by Denoising. NeurIPS (2021)
Lantao Yu, Jiaming Song, Yang Song, Stefano Ermon
Pseudo-Spherical Contrastive Divergence. CoRR (2021)
Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon
Improved Autoregressive Modeling with Distribution Smoothing. CoRR (2021)
Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon
Anytime Sampling for Autoregressive Models via Ordered Autoencoding. CoRR (2021)
Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation. NeurIPS (2021)
Kuno Kim, Akshat Jindal, Yang Song, Jiaming Song, Yanan Sui, Stefano Ermon
Imitation with Neural Density Models. NeurIPS (2021)
Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon
Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving. ICML (2021)
Yang Song, Diederik P. Kingma
How to Train Your Energy-Based Models. CoRR (2021)
Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation. CoRR (2021)
Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma
Learning Energy-Based Models by Diffusion Recovery Likelihood. ICLR (2021)
Lantao Yu, Jiaming Song, Yang Song, Stefano Ermon
Pseudo-Spherical Contrastive Divergence. NeurIPS (2021)
Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon
Anytime Sampling for Autoregressive Models via Ordered Autoencoding. ICLR (2021)
Kristy Choi, Chenlin Meng, Yang Song, Stefano Ermon
Density Ratio Estimation via Infinitesimal Classification. CoRR (2021)
Chenlin Meng, Yang Song, Wenzhe Li, Stefano Ermon
Estimating High Order Gradients of the Data Distribution by Denoising. CoRR (2021)
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
Score-Based Generative Modeling through Stochastic Differential Equations. ICLR (2021)
Yang Song, Liyue Shen, Lei Xing, Stefano Ermon
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models. CoRR (2021)
Chenlin Meng, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon
SDEdit: Image Synthesis and Editing with Stochastic Differential Equations. CoRR (2021)
Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon
Training Deep Energy-Based Models with f-Divergence Minimization. ICML (2020)
Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon
Nonlinear Equation Solving: A Faster Alternative to Feedforward Computation. CoRR (2020)
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
Score-Based Generative Modeling through Stochastic Differential Equations. CoRR (2020)
Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon
Autoregressive Score Matching. CoRR (2020)
Yusuke Tashiro, Yang Song, Stefano Ermon
Output Diversified Initialization for Adversarial Attacks. CoRR (2020)
Kuno Kim, Akshat Jindal, Yang Song, Jiaming Song, Yanan Sui, Stefano Ermon
Imitation with Neural Density Models. CoRR (2020)
Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon
Autoregressive Score Matching. NeurIPS (2020)
Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon
Permutation Invariant Graph Generation via Score-Based Generative Modeling. CoRR (2020)
Tianyu Pang, Kun Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu
Efficient Learning of Generative Models via Finite-Difference Score Matching. CoRR (2020)
Tianyu Pang, Taufik Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu
Efficient Learning of Generative Models via Finite-Difference Score Matching. NeurIPS (2020)
Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma
Learning Energy-Based Models by Diffusion Recovery Likelihood. CoRR (2020)
Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon
Training Deep Energy-Based Models with f-Divergence Minimization. CoRR (2020)
Yang Song, Stefano Ermon
Improved Techniques for Training Score-Based Generative Models. NeurIPS (2020)
Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon
Permutation Invariant Graph Generation via Score-Based Generative Modeling. AISTATS (2020)
Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon
Gaussianization Flows. CoRR (2020)
Laëtitia Shao, Yang Song, Stefano Ermon
Understanding Classifier Mistakes with Generative Models. CoRR (2020)
Yusuke Tashiro, Yang Song, Stefano Ermon
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks. NeurIPS (2020)
Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon
Gaussianization Flows. AISTATS (2020)
Yang Song, Stefano Ermon
Improved Techniques for Training Score-Based Generative Models. CoRR (2020)
Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon
Sliced Score Matching: A Scalable Approach to Density and Score Estimation. UAI (2019)
Yang Song, Stefano Ermon
Generative Modeling by Estimating Gradients of the Data Distribution. CoRR (2019)
Yang Song, Chenlin Meng, Stefano Ermon
MintNet: Building Invertible Neural Networks with Masked Convolutions. CoRR (2019)
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel
Efficient Graph Generation with Graph Recurrent Attention Networks. CoRR (2019)
Jiaming Song, Yang Song, Stefano Ermon
Unsupervised Out-of-Distribution Detection with Batch Normalization. CoRR (2019)
Yang Song, Chenlin Meng, Stefano Ermon
MintNet: Building Invertible Neural Networks with Masked Convolutions. NeurIPS (2019)
Yang Song, Stefano Ermon
Generative Modeling by Estimating Gradients of the Data Distribution. NeurIPS (2019)
Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon
Sliced Score Matching: A Scalable Approach to Density and Score Estimation. CoRR (2019)
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel
Efficient Graph Generation with Graph Recurrent Attention Networks. NeurIPS (2019)
Yang Song, Rui Shu, Nate Kushman, Stefano Ermon
Generative Adversarial Examples. CoRR (2018)
Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples. ICLR (Poster) (2018)
Yang Song, Stefano Ermon
Accelerating Natural Gradient with Higher-Order Invariance. CoRR (2018)
Yang Song, Rui Shu, Nate Kushman, Stefano Ermon
Constructing Unrestricted Adversarial Examples with Generative Models. NeurIPS (2018)
Yang Song, Jiaming Song, Stefano Ermon
Accelerating Natural Gradient with Higher-Order Invariance. ICML (2018)
Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples. CoRR (2017)