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ORCIDYang Song 0011
Person information
- affiliation: Stanford University, CA, USA
Other persons with the same name
- Yang Song — disambiguation page
- Yang Song 0001
![[0000-0003-1283-1672]](https://dblp.org/img/orcid-mark.12x12.png "0000-0003-1283-1672")
— University of New South Wales, Sydney, NSW, Australia (and 1 more) - Yang Song 0002 — Waseda University, Graduate School of Information, Production and Systems, Kitakyushu City, Japan
- Yang Song 0003 — Shanghai University, School of Mechatronics Engineering and Automation, Department of Automation, China (and 1 more)
- Yang Song 0004 — Beihang University, School of Instrumentation Science and Opto-Electronics Engineering, Beijing, China
- Yang Song 0005 — IBM T. J. Watson Research Center, Hawthorne, NY, USA (and 1 more)
- Yang Song 0006 — University of California, San Diego, CA, USA
- Yang Song 0007 — University of Arizona, Department of Electrical and Computer Engineering, Tucson, AZ, USA
- Yang Song 0008 — Kuaishou Technology, Beijing, China (and 3 more)
- Yang Song 0009 — Google Research, Mountain View, CA, USA
- Yang Song 0010 — East China Normal University, School of Computer Science and Software Engineering, Shanghai, China
- Yang Song 0012 — Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore (and 2 more)
- Yang Song 0013 — University of Tennessee, Department of Electrical Engineering and Computer Science, xKnoxville, USA (and 2 more)
- Yang Song 0014 — Norwegian University of Science and Technology, Trondheim, Norway (and 2 more)
- Yang Song 0015 — Ningbo University, Faculty of Information Science and Engineering, College of Science and Technology, Zhejiang Provincial United Key Laboratory of Embedded Systems, China
- Yang Song 0016 — University of Utah, School of Computing, Salt Lake City, UH, USA
- Yang Song 0017 — Zoomlion Smart Agriculture, Intelligent Agriculture Research Institute, Wuhu, China (and 1 more)
- Yang Song 0018 — China Agricultural University, College of Resources and Environmental Sciences, Beijing, China
- Yang Song 0019 — University of North Carolina at Wilmington, Department of Computer Science, NC, USA (and 1 more)
- Yang Song 0020 — Communication University of China, School of Information Engineering, China
- Yang Song 0021 — BOSS Zhipin NLP Center, Beijing, China
- Yang Song 0022 — Northeastern University, College of Information Science and Engineering, Shenyang, China
- Yang Song 0023 — Fujifilm Software, Inc., San Jose, CA, USA (and 1 more)
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2020 – today
- 2024
[j1]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): 105:1-105:39 (2024)- [i36]Bingliang Zhang, Wenda Chu, Julius Berner, Chenlin Meng, Anima Anandkumar, Yang Song:
Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing. CoRR abs/2407.01521 (2024) - 2022
- [c32]Kristy Choi, Chenlin Meng, Yang Song, Stefano Ermon:
Density Ratio Estimation via Infinitesimal Classification. AISTATS 2022: 2552-2573 - [c31]Yang Song, Liyue Shen, Lei Xing, Stefano Ermon:
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models. ICLR 2022 - [c30]Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon:
SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations. ICLR 2022 - [c29]Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang:
GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation. ICLR 2022 - [i35]Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang:
GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation. CoRR abs/2203.02923 (2022) - [i34]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 abs/2209.00796 (2022) - [i33]Ling Yang, Zhilin Huang, Yang Song, Shenda Hong, Guohao Li, Wentao Zhang, Bin Cui, Bernard Ghanem, Ming-Hsuan Yang:
Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training. CoRR abs/2211.11138 (2022) - 2021
- [c28]Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole:
Score-Based Generative Modeling through Stochastic Differential Equations. ICLR 2021 - [c27]Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma:
Learning Energy-Based Models by Diffusion Recovery Likelihood. ICLR 2021 - [c26]Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon:
Improved Autoregressive Modeling with Distribution Smoothing. ICLR 2021 - [c25]Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon:
Anytime Sampling for Autoregressive Models via Ordered Autoencoding. ICLR 2021 - [c24]Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon:
Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving. ICML 2021: 9791-9800 - [c23]Yang Song, Conor Durkan, Iain Murray, Stefano Ermon:
Maximum Likelihood Training of Score-Based Diffusion Models. NeurIPS 2021: 1415-1428 - [c22]Kuno Kim, Akshat Jindal, Yang Song, Jiaming Song, Yanan Sui, Stefano Ermon:
Imitation with Neural Density Models. NeurIPS 2021: 5360-5372 - [c21]Lantao Yu, Jiaming Song, Yang Song, Stefano Ermon:
Pseudo-Spherical Contrastive Divergence. NeurIPS 2021: 22348-22362 - [c20]Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon:
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation. NeurIPS 2021: 24804-24816 - [c19]Chenlin Meng, Yang Song, Wenzhe Li, Stefano Ermon:
Estimating High Order Gradients of the Data Distribution by Denoising. NeurIPS 2021: 25359-25369 - [i32]Yang Song, Diederik P. Kingma:
How to Train Your Energy-Based Models. CoRR abs/2101.03288 (2021) - [i31]Conor Durkan, Yang Song:
On Maximum Likelihood Training of Score-Based Generative Models. CoRR abs/2101.09258 (2021) - [i30]Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon:
Anytime Sampling for Autoregressive Models via Ordered Autoencoding. CoRR abs/2102.11495 (2021) - [i29]Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon:
Improved Autoregressive Modeling with Distribution Smoothing. CoRR abs/2103.15089 (2021) - [i28]Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon:
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation. CoRR abs/2107.03502 (2021) - [i27]Chenlin Meng, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon:
SDEdit: Image Synthesis and Editing with Stochastic Differential Equations. CoRR abs/2108.01073 (2021) - [i26]Lantao Yu, Jiaming Song, Yang Song, Stefano Ermon:
Pseudo-Spherical Contrastive Divergence. CoRR abs/2111.00780 (2021) - [i25]Chenlin Meng, Yang Song, Wenzhe Li, Stefano Ermon:
Estimating High Order Gradients of the Data Distribution by Denoising. CoRR abs/2111.04726 (2021) - [i24]Yang Song, Liyue Shen, Lei Xing, Stefano Ermon:
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models. CoRR abs/2111.08005 (2021) - [i23]Kristy Choi, Chenlin Meng, Yang Song, Stefano Ermon:
Density Ratio Estimation via Infinitesimal Classification. CoRR abs/2111.11010 (2021) - 2020
- [c18]Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon:
Gaussianization Flows. AISTATS 2020: 4336-4345 - [c17]Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon:
Permutation Invariant Graph Generation via Score-Based Generative Modeling. AISTATS 2020: 4474-4484 - [c16]Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon:
Training Deep Energy-Based Models with f-Divergence Minimization. ICML 2020: 10957-10967 - [c15]Yang Song, Stefano Ermon:
Improved Techniques for Training Score-Based Generative Models. NeurIPS 2020 - [c14]Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon:
Autoregressive Score Matching. NeurIPS 2020 - [c13]Tianyu Pang, Taufik Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu:
Efficient Learning of Generative Models via Finite-Difference Score Matching. NeurIPS 2020 - [c12]Yusuke Tashiro, Yang Song, Stefano Ermon:
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks. NeurIPS 2020 - [i22]Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon:
Nonlinear Equation Solving: A Faster Alternative to Feedforward Computation. CoRR abs/2002.03629 (2020) - [i21]Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon:
Permutation Invariant Graph Generation via Score-Based Generative Modeling. CoRR abs/2003.00638 (2020) - [i20]Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon:
Gaussianization Flows. CoRR abs/2003.01941 (2020) - [i19]Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon:
Training Deep Energy-Based Models with f-Divergence Minimization. CoRR abs/2003.03463 (2020) - [i18]Yusuke Tashiro, Yang Song, Stefano Ermon:
Output Diversified Initialization for Adversarial Attacks. CoRR abs/2003.06878 (2020) - [i17]Yang Song, Stefano Ermon:
Improved Techniques for Training Score-Based Generative Models. CoRR abs/2006.09011 (2020) - [i16]Tianyu Pang, Kun Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu:
Efficient Learning of Generative Models via Finite-Difference Score Matching. CoRR abs/2007.03317 (2020) - [i15]Laëtitia Shao, Yang Song, Stefano Ermon:
Understanding Classifier Mistakes with Generative Models. CoRR abs/2010.02364 (2020) - [i14]Kuno Kim, Akshat Jindal, Yang Song, Jiaming Song, Yanan Sui, Stefano Ermon:
Imitation with Neural Density Models. CoRR abs/2010.09808 (2020) - [i13]Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon:
Autoregressive Score Matching. CoRR abs/2010.12810 (2020) - [i12]Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole:
Score-Based Generative Modeling through Stochastic Differential Equations. CoRR abs/2011.13456 (2020) - [i11]Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma:
Learning Energy-Based Models by Diffusion Recovery Likelihood. CoRR abs/2012.08125 (2020)
2010 – 2019
- 2019
- [c11]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: 4257-4267 - [c10]Yang Song, Chenlin Meng, Stefano Ermon:
MintNet: Building Invertible Neural Networks with Masked Convolutions. NeurIPS 2019: 11002-11012 - [c9]Yang Song, Stefano Ermon:
Generative Modeling by Estimating Gradients of the Data Distribution. NeurIPS 2019: 11895-11907 - [c8]Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon:
Sliced Score Matching: A Scalable Approach to Density and Score Estimation. UAI 2019: 574-584 - [i10]Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon:
Sliced Score Matching: A Scalable Approach to Density and Score Estimation. CoRR abs/1905.07088 (2019) - [i9]Yang Song, Stefano Ermon:
Generative Modeling by Estimating Gradients of the Data Distribution. CoRR abs/1907.05600 (2019) - [i8]Yang Song, Chenlin Meng, Stefano Ermon:
MintNet: Building Invertible Neural Networks with Masked Convolutions. CoRR abs/1907.07945 (2019) - [i7]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 abs/1910.00760 (2019) - [i6]Jiaming Song, Yang Song, Stefano Ermon:
Unsupervised Out-of-Distribution Detection with Batch Normalization. CoRR abs/1910.09115 (2019) - 2018
- [c7]Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman:
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples. ICLR (Poster) 2018 - [c6]Yang Song, Jiaming Song, Stefano Ermon:
Accelerating Natural Gradient with Higher-Order Invariance. ICML 2018: 4720-4729 - [c5]Yang Song, Rui Shu, Nate Kushman, Stefano Ermon:
Constructing Unrestricted Adversarial Examples with Generative Models. NeurIPS 2018: 8322-8333 - [i5]Yang Song, Stefano Ermon:
Accelerating Natural Gradient with Higher-Order Invariance. CoRR abs/1803.01273 (2018) - [i4]Yang Song, Rui Shu, Nate Kushman, Stefano Ermon:
Generative Adversarial Examples. CoRR abs/1805.07894 (2018) - 2017
- [i3]Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman:
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples. CoRR abs/1710.10766 (2017) - 2016
- [c4]Yang Song, Jun Zhu:
Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization. AAAI 2016: 2044-2050 - [c3]Yang Song, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun:
Training Deep Neural Networks via Direct Loss Minimization. ICML 2016: 2169-2177 - [c2]Chang Liu, Jun Zhu, Yang Song:
Stochastic Gradient Geodesic MCMC Methods. NIPS 2016: 3009-3017 - [c1]Yang Song, Jun Zhu, Yong Ren:
Kernel Bayesian Inference with Posterior Regularization. NIPS 2016: 4763-4771 - 2015
- [i2]Yang Song, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun:
Direct Loss Minimization for Training Deep Neural Nets. CoRR abs/1511.06411 (2015) - [i1]Yang Song, Jun Zhu:
Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization. CoRR abs/1512.01110 (2015)
Coauthor Index
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