Tim Salimans
Tim is a Machine Learning research scientist working on generative modeling. He is well known for his work on GANs and VAEs, and their evaluation using the Inception score, as well as his work on autoregressive generative models like GPT-1 and PixelCNN++. More recently, he has been focusing on diffusion models for generating images (Imagen) and video (Imagen Video), and on making these models fast to sample using distillation.
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Palette: Image-to-Image Diffusion Models
Chitwan Saharia
Chris A. Lee
Huiwen Chang
Jonathan Ho
Mohammad Norouzi
William Chan
SIGGRAPH 2022 (2022)
Image Super-Resolution via Iterative Refinement
Chitwan Saharia
Jonathan Ho
Mohammad Norouzi
William Chan
Submission to ICCV 2021
Variational Diffusion Models
Diederik P. Kingma
Jonathan Ho
Advances in Neural Information Processing Systems 34 (NeurIPS 2021) (2021)
Cascaded Diffusion Models for High Fidelity Image Generation
Jonathan Ho
Chitwan Saharia
William Chan
Mohammad Norouzi
https://cascaded-diffusion.github.io/ (2021)
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression
Rianne van den Berg
Casper Kaae Sønderby
ICLR 2021, ICLR 2021 (to appear)
A Spectral Energy Distance for Parallel Speech Synthesis
Nal Kalchbrenner
Rianne van den Berg
(2020)
MetNet: A Neural Weather Model for Precipitation Forecasting
Casper Kaae Sønderby
Lasse Espeholt
Avital Oliver
Jason Hickey
Nal Kalchbrenner
Submission to journal (2020)
