Stebėti
Yang Song
Yang Song
OpenAI
Patvirtintas el. paštas openai.com - Pagrindinis puslapis
Pavadinimas
Cituota
Cituota
Metai
Score-Based Generative Modeling through Stochastic Differential Equations
Y Song, J Sohl-Dickstein, DP Kingma, A Kumar, S Ermon, B Poole
International Conference on Learning Representations, 2021
38912021
Gpt-4 technical report
J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ...
arXiv preprint arXiv:2303.08774, 2023
3154*2023
Generative modeling by estimating gradients of the data distribution
Y Song, S Ermon
Advances in Neural Information Processing Systems, 11918-11930, 2019
27392019
SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
C Meng, Y He, Y Song, J Song, J Wu, JY Zhu, S Ermon
International Conference on Learning Representations, 2021
1132*2021
Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Y Song, T Kim, S Nowozin, S Ermon, N Kushman
International Conference on Learning Representations, 2018
9022018
Diffusion models: A comprehensive survey of methods and applications
L Yang, Z Zhang, Y Song, S Hong, R Xu, Y Zhao, W Zhang, B Cui, ...
ACM Computing Surveys, 2022
8972022
Improved techniques for training score-based generative models
Y Song, S Ermon
Advances in Neural Information Processing Systems 33, 2020
8792020
Maximum Likelihood Training of Score-Based Diffusion Models
Y Song, C Durkan, I Murray, S Ermon
arXiv preprint arXiv:2101.09258, 2021
4652021
Consistency Models
Y Song, P Dhariwal, M Chen, I Sutskever
arXiv preprint arXiv:2303.01469, 2023
4312023
GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation
M Xu, L Yu, Y Song, C Shi, S Ermon, J Tang
International Conference on Learning Representations, 2021
4062021
Sliced score matching: A scalable approach to density and score estimation
Y Song, S Garg, J Shi, S Ermon
Uncertainty in Artificial Intelligence, 574-584, 2019
3762019
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models
Y Song, L Shen, L Xing, S Ermon
arXiv preprint arXiv:2111.08005, 2021
3532021
Efficient graph generation with graph recurrent attention networks
R Liao, Y Li, Y Song, S Wang, W Hamilton, DK Duvenaud, R Urtasun, ...
Advances in Neural Information Processing Systems, 4255-4265, 2019
3412019
Constructing Unrestricted Adversarial Examples with Generative Models
Y Song, R Shu, N Kushman, S Ermon
Advances in Neural Information Processing Systems, 8322-8333, 2018
3252018
CSDI: Conditional score-based diffusion models for probabilistic time series imputation
Y Tashiro, J Song, Y Song, S Ermon
Advances in Neural Information Processing Systems 34, 24804-24816, 2021
3242021
How to Train Your Energy-Based Models
Y Song, DP Kingma
arXiv preprint arXiv:2101.03288, 2021
2332021
Permutation invariant graph generation via score-Based generative modeling
C Niu, Y Song, J Song, S Zhao, A Grover, S Ermon
International Conference on Artificial Intelligence and Statistics, 4474-4484, 2020
1892020
Training deep neural networks via direct loss minimization
Y Song, A Schwing, R Zemel, R Urtasun
International Conference on Machine Learning, 2169-2177, 2016
1182016
Learning Energy-Based Models by Diffusion Recovery Likelihood
R Gao, Y Song, B Poole, YN Wu, DP Kingma
International Conference on Learning Representations, 2020
1142020
Diversity can be Transferred: Output Diversification for White-and Black-box Attacks
Y Tashiro, Y Song, S Ermon
Advances in Neural Information Processing Systems 33, 2020
100*2020
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