This year’s #ML4H2021 best paper was awarded to SeqMRI, which learns how to sequentially select future measurements based on previously collected samples to improve accelerated MRI. Authors: Tianwei Yin, Zihui (Ray) Wu, He Sun, Adrian Dalca, Yisong Yue, and Katie Bouman

The best paper of #ML4H2021 was awarded to SeqMRI, which learns to sequentially select future measurements based on previous samples to improve accelerated MRI. Authors: Tianwei Yin, Zihui (Ray) Wu, He Sun, Adrian Dalca, Yisong Yue, Katie Bouman Website: imaging.cms.caltech.edu/seq-mri/

Check out the fun projects we are working on in my computational cameras group at Caltech! computationalcameras.org

"Hopfield joined Caltech as faculty in 1980 and, two years later, published his seminal paper in which he applied principles of the brain to computer circuits, creating a neural network able to hold memory and recognize patterns." latimes.com/science/story/…

It may be necessary to align human behavior with AI feedback. #NeurIPS2024

TLDR: if you can train video diffusion priors, then solving video inverse problems can be done much simpler and more accurately than heavily engineered approaches based on image diffusion priors. Examples below for black hole video reconstruction and dynamic MRI.

💻 Code & Data Modular, extensible codebase—plug in your own PnPDP methods or new inverse problems! 👉 GitHub: github.com/devzhk/Inverse….

🚨Our new #ICML2025 paper is featured by MIT News! news.mit.edu/2025/unpacking… Why are LLMs lost in the middle? Why do transformers systematically favor tokens at certain positions in a sequence? We had vague intuitions but no rigorous understandings, until now! We develop a