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Yang Song is a leading expert in generative models, especially score-based diffusion models. He works on improving generative models for diverse modalities, applications, and scientific discovery.
- Blog
Sliced Score Matching: A Scalable Approach to Density and...
- Publications
Yang Song*, Sahaj Garg*, Jiaxin Shi, and Stefano Ermon. In...
- Repositories
repositories. Most of my research has code open-sourced at...
- cv
The personal website of Yang Song. ... Organizer: NeurIPS...
- Blog
Learn how to generate samples from data distributions by estimating their score functions, which are gradients of log probability density functions. Score-based models have advantages over existing generative models, such as GANs, in terms of sample quality, flexibility, and inverse problem solving.
Sep 4, 2018 · Yang Song is a researcher and blogger in generative modeling and machine learning. His blog posts cover topics such as score-based generative models, sliced score matching, natural gradient, and density estimation.
Yang Song. Other names宋飏. OpenAI.
Yang Song is a research scientist at Google Inc. and a co-author of many papers on computer vision and machine learning topics. See his publications, citations, h-index, i10-index, and co-authors on Google Scholar.
Yang Song is a researcher at Google and a co-author of 174 articles on topics such as information retrieval, machine learning, and recommender systems. See his full profile, co-authors, and publications sorted by citations, year, and title.
Song Yang. PhD, University of Auckland. Verified email at aucklanduni.ac.nz. Machine Learning Deep Learning Spatio-temporal Data Mining AI in Healthcare.
