Yang Song - RankMe

73 papers A* 26A 4Journal 40Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing. CVPR Bingliang Zhang, Wenda Chu, Julius Berner, Chenlin Meng, Anima Anandkumar, Yang Song
2025	J	`jnl`	The Principles of Diffusion Models. CoRR Chieh-Hsin Lai, Yang Song, Dongjun Kim, Yuki Mitsufuji, Stefano Ermon
2024	J	`jnl`	Diffusion Models: A Comprehensive Survey of Methods and Applications. ACM Comput. Surv. Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, Ming-Hsuan Yang
2024	J	`jnl`	Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing. CoRR Bingliang Zhang, Wenda Chu, Julius Berner, Chenlin Meng, Anima Anandkumar, Yang Song
2024	J	`jnl`	SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation. Trans. Mach. Learn. Res. Qi Yan, Zhengyang Liang, Yang Song, Renjie Liao, Lele Wang
2023	J	`jnl`	SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation. CoRR Qi Yan, Zhengyang Liang, Yang Song, Renjie Liao, Lele Wang
2022	A	`conf`	Density Ratio Estimation via Infinitesimal Classification. AISTATS Kristy Choi, Chenlin Meng, Yang Song, Stefano Ermon
2022	J	`jnl`	Diffusion Models: A Comprehensive Survey of Methods and Applications. CoRR Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Ming-Hsuan Yang, Bin Cui
2022	J	`jnl`	Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training. CoRR Ling Yang, Zhilin Huang, Yang Song, Shenda Hong, Guohao Li, Wentao Zhang, Bin Cui, Bernard Ghanem, Ming-Hsuan Yang
2022	A*	`conf`	GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation. ICLR Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang
2022	J	`jnl`	GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation. CoRR Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang
2022	A*	`conf`	SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations. ICLR Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon
2022	A*	`conf`	Solving Inverse Problems in Medical Imaging with Score-Based Generative Models. ICLR Yang Song, Liyue Shen, Lei Xing, Stefano Ermon
2021	A*	`conf`	Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving. ICML Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon
2021	A*	`conf`	Anytime Sampling for Autoregressive Models via Ordered Autoencoding. ICLR Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon
2021	J	`jnl`	Anytime Sampling for Autoregressive Models via Ordered Autoencoding. CoRR Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon
2021	A*	`conf`	CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation. NeurIPS Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon
2021	J	`jnl`	CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation. CoRR Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon
2021	J	`jnl`	Density Ratio Estimation via Infinitesimal Classification. CoRR Kristy Choi, Chenlin Meng, Yang Song, Stefano Ermon
2021	A*	`conf`	Estimating High Order Gradients of the Data Distribution by Denoising. NeurIPS Chenlin Meng, Yang Song, Wenzhe Li, Stefano Ermon
2021	J	`jnl`	Estimating High Order Gradients of the Data Distribution by Denoising. CoRR Chenlin Meng, Yang Song, Wenzhe Li, Stefano Ermon
2021	J	`jnl`	How to Train Your Energy-Based Models. CoRR Yang Song, Diederik P. Kingma
2021	A*	`conf`	Imitation with Neural Density Models. NeurIPS Kuno Kim, Akshat Jindal, Yang Song, Jiaming Song, Yanan Sui, Stefano Ermon
2021	A*	`conf`	Improved Autoregressive Modeling with Distribution Smoothing. ICLR Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon
2021	J	`jnl`	Improved Autoregressive Modeling with Distribution Smoothing. CoRR Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon
2021	A*	`conf`	Learning Energy-Based Models by Diffusion Recovery Likelihood. ICLR Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma
2021	A*	`conf`	Maximum Likelihood Training of Score-Based Diffusion Models. NeurIPS Yang Song, Conor Durkan, Iain Murray, Stefano Ermon
2021	J	`jnl`	On Maximum Likelihood Training of Score-Based Generative Models. CoRR Conor Durkan, Yang Song
2021	A*	`conf`	Pseudo-Spherical Contrastive Divergence. NeurIPS Lantao Yu, Jiaming Song, Yang Song, Stefano Ermon
2021	J	`jnl`	Pseudo-Spherical Contrastive Divergence. CoRR Lantao Yu, Jiaming Song, Yang Song, Stefano Ermon
2021	J	`jnl`	SDEdit: Image Synthesis and Editing with Stochastic Differential Equations. CoRR Chenlin Meng, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon
2021	A*	`conf`	Score-Based Generative Modeling through Stochastic Differential Equations. ICLR Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
2021	J	`jnl`	Solving Inverse Problems in Medical Imaging with Score-Based Generative Models. CoRR Yang Song, Liyue Shen, Lei Xing, Stefano Ermon
2020	A*	`conf`	Autoregressive Score Matching. NeurIPS Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon
2020	J	`jnl`	Autoregressive Score Matching. CoRR Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon
2020	A*	`conf`	Diversity can be Transferred: Output Diversification for White- and Black-box Attacks. NeurIPS Yusuke Tashiro, Yang Song, Stefano Ermon
2020	A*	`conf`	Efficient Learning of Generative Models via Finite-Difference Score Matching. NeurIPS Tianyu Pang, Taufik Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu
2020	J	`jnl`	Efficient Learning of Generative Models via Finite-Difference Score Matching. CoRR Tianyu Pang, Kun Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu
2020	A	`conf`	Gaussianization Flows. AISTATS Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon
2020	J	`jnl`	Gaussianization Flows. CoRR Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon
2020	J	`jnl`	Imitation with Neural Density Models. CoRR Kuno Kim, Akshat Jindal, Yang Song, Jiaming Song, Yanan Sui, Stefano Ermon
2020	A*	`conf`	Improved Techniques for Training Score-Based Generative Models. NeurIPS Yang Song, Stefano Ermon
2020	J	`jnl`	Improved Techniques for Training Score-Based Generative Models. CoRR Yang Song, Stefano Ermon
2020	J	`jnl`	Learning Energy-Based Models by Diffusion Recovery Likelihood. CoRR Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma
2020	J	`jnl`	Nonlinear Equation Solving: A Faster Alternative to Feedforward Computation. CoRR Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon
2020	J	`jnl`	Output Diversified Initialization for Adversarial Attacks. CoRR Yusuke Tashiro, Yang Song, Stefano Ermon
2020	A	`conf`	Permutation Invariant Graph Generation via Score-Based Generative Modeling. AISTATS Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon
2020	J	`jnl`	Permutation Invariant Graph Generation via Score-Based Generative Modeling. CoRR Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon
2020	J	`jnl`	Score-Based Generative Modeling through Stochastic Differential Equations. CoRR Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
2020	A*	`conf`	Training Deep Energy-Based Models with f-Divergence Minimization. ICML Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon
2020	J	`jnl`	Training Deep Energy-Based Models with f-Divergence Minimization. CoRR Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon
2020	J	`jnl`	Understanding Classifier Mistakes with Generative Models. CoRR Laëtitia Shao, Yang Song, Stefano Ermon
2019	A*	`conf`	Efficient Graph Generation with Graph Recurrent Attention Networks. NeurIPS Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel
2019	J	`jnl`	Efficient Graph Generation with Graph Recurrent Attention Networks. CoRR Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel
2019	A*	`conf`	Generative Modeling by Estimating Gradients of the Data Distribution. NeurIPS Yang Song, Stefano Ermon
2019	J	`jnl`	Generative Modeling by Estimating Gradients of the Data Distribution. CoRR Yang Song, Stefano Ermon
2019	A*	`conf`	MintNet: Building Invertible Neural Networks with Masked Convolutions. NeurIPS Yang Song, Chenlin Meng, Stefano Ermon
2019	J	`jnl`	MintNet: Building Invertible Neural Networks with Masked Convolutions. CoRR Yang Song, Chenlin Meng, Stefano Ermon
2019	A	`conf`	Sliced Score Matching: A Scalable Approach to Density and Score Estimation. UAI Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon
2019	J	`jnl`	Sliced Score Matching: A Scalable Approach to Density and Score Estimation. CoRR Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon
2019	J	`jnl`	Unsupervised Out-of-Distribution Detection with Batch Normalization. CoRR Jiaming Song, Yang Song, Stefano Ermon
2018	A*	`conf`	Accelerating Natural Gradient with Higher-Order Invariance. ICML Yang Song, Jiaming Song, Stefano Ermon
2018	J	`jnl`	Accelerating Natural Gradient with Higher-Order Invariance. CoRR Yang Song, Stefano Ermon
2018	A*	`conf`	Constructing Unrestricted Adversarial Examples with Generative Models. NeurIPS Yang Song, Rui Shu, Nate Kushman, Stefano Ermon
2018	J	`jnl`	Generative Adversarial Examples. CoRR Yang Song, Rui Shu, Nate Kushman, Stefano Ermon
2018	—	`conf`	PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples. ICLR (Poster) Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman
2017	J	`jnl`	PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples. CoRR Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman
2016	A*	`conf`	Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization. AAAI Yang Song, Jun Zhu
2016	—	`conf`	Kernel Bayesian Inference with Posterior Regularization. NIPS Yang Song, Jun Zhu, Yong Ren
2016	—	`conf`	Stochastic Gradient Geodesic MCMC Methods. NIPS Chang Liu, Jun Zhu, Yang Song
2016	A*	`conf`	Training Deep Neural Networks via Direct Loss Minimization. ICML Yang Song, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun
2015	J	`jnl`	Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization. CoRR Yang Song, Jun Zhu
2015	J	`jnl`	Direct Loss Minimization for Training Deep Neural Nets. CoRR Yang Song, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun