Yang Song

According to our database, Yang Song authored at least 48 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Diffusion Models: A Comprehensive Survey of Methods and Applications.

Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, Ming-Hsuan Yang

ACM Comput. Surv., April, 2024

Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing.

Bingliang Zhang, Wenda Chu, Julius Berner, Chenlin Meng, Anima Anandkumar, Yang Song

CoRR, 2024

2022

Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training.

Ling Yang, Zhilin Huang, Yang Song, Shenda Hong, Guohao Li, Wentao Zhang, Bin Cui, Bernard Ghanem, Ming-Hsuan Yang

CoRR, 2022

Diffusion Models: A Comprehensive Survey of Methods and Applications.

Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Ming-Hsuan Yang, Bin Cui

CoRR, 2022

GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation.

Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang

Proceedings of the Tenth International Conference on Learning Representations, 2022

SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations.

Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon

Proceedings of the Tenth International Conference on Learning Representations, 2022

Solving Inverse Problems in Medical Imaging with Score-Based Generative Models.

Yang Song, Liyue Shen, Lei Xing, Stefano Ermon

Proceedings of the Tenth International Conference on Learning Representations, 2022

Density Ratio Estimation via Infinitesimal Classification.

Kristy Choi, Chenlin Meng, Yang Song, Stefano Ermon

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

SDEdit: Image Synthesis and Editing with Stochastic Differential Equations.

Chenlin Meng, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon

CoRR, 2021

On Maximum Likelihood Training of Score-Based Generative Models.

Conor Durkan, Yang Song

CoRR, 2021

How to Train Your Energy-Based Models.

Yang Song, Diederik P. Kingma

CoRR, 2021

Pseudo-Spherical Contrastive Divergence.

Lantao Yu, Jiaming Song, Yang Song, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation.

Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Maximum Likelihood Training of Score-Based Diffusion Models.

Yang Song, Conor Durkan, Iain Murray, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Estimating High Order Gradients of the Data Distribution by Denoising.

Chenlin Meng, Yang Song, Wenzhe Li, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Imitation with Neural Density Models.

Kuno Kim, Akshat Jindal, Yang Song, Jiaming Song, Yanan Sui, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving.

Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon

Proceedings of the 38th International Conference on Machine Learning, 2021

Anytime Sampling for Autoregressive Models via Ordered Autoencoding.

Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon

Proceedings of the 9th International Conference on Learning Representations, 2021

Improved Autoregressive Modeling with Distribution Smoothing.

Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon

Proceedings of the 9th International Conference on Learning Representations, 2021

Learning Energy-Based Models by Diffusion Recovery Likelihood.

Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma

Proceedings of the 9th International Conference on Learning Representations, 2021

Score-Based Generative Modeling through Stochastic Differential Equations.

Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Understanding Classifier Mistakes with Generative Models.

Laëtitia Shao, Yang Song, Stefano Ermon

CoRR, 2020

Efficient Learning of Generative Models via Finite-Difference Score Matching.

Tianyu Pang, Kun Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu

CoRR, 2020

Output Diversified Initialization for Adversarial Attacks.

Yusuke Tashiro, Yang Song, Stefano Ermon

CoRR, 2020

Nonlinear Equation Solving: A Faster Alternative to Feedforward Computation.

Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon

CoRR, 2020

Diversity can be Transferred: Output Diversification for White- and Black-box Attacks.

Yusuke Tashiro, Yang Song, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Efficient Learning of Generative Models via Finite-Difference Score Matching.

Tianyu Pang, Taufik Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Autoregressive Score Matching.

Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Improved Techniques for Training Score-Based Generative Models.

Yang Song, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Training Deep Energy-Based Models with f-Divergence Minimization.

Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon

Proceedings of the 37th International Conference on Machine Learning, 2020

Permutation Invariant Graph Generation via Score-Based Generative Modeling.

Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Gaussianization Flows.

Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Unsupervised Out-of-Distribution Detection with Batch Normalization.

Jiaming Song, Yang Song, Stefano Ermon

CoRR, 2019

Efficient Graph Generation with Graph Recurrent Attention Networks.

Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel

CoRR, 2019

Sliced Score Matching: A Scalable Approach to Density and Score Estimation.

Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

MintNet: Building Invertible Neural Networks with Masked Convolutions.

Yang Song, Chenlin Meng, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Generative Modeling by Estimating Gradients of the Data Distribution.

Yang Song, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Efficient Graph Generation with Graph Recurrent Attention Networks.

Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Generative Adversarial Examples.

Yang Song, Rui Shu, Nate Kushman, Stefano Ermon

CoRR, 2018

Accelerating Natural Gradient with Higher-Order Invariance.

Yang Song, Stefano Ermon

CoRR, 2018

Constructing Unrestricted Adversarial Examples with Generative Models.

Yang Song, Rui Shu, Nate Kushman, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Accelerating Natural Gradient with Higher-Order Invariance.

Yang Song, Jiaming Song, Stefano Ermon

Proceedings of the 35th International Conference on Machine Learning, 2018

PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples.

Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman

Proceedings of the 6th International Conference on Learning Representations, 2018

2016

Kernel Bayesian Inference with Posterior Regularization.

Yang Song, Jun Zhu, Yong Ren

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Stochastic Gradient Geodesic MCMC Methods.

Chang Liu, Jun Zhu, Yang Song

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Training Deep Neural Networks via Direct Loss Minimization.

Yang Song, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun

Proceedings of the 33nd International Conference on Machine Learning, 2016

Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization.

Yang Song, Jun Zhu

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Direct Loss Minimization for Training Deep Neural Nets.

Yang Song, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun

CoRR, 2015