Plug-and-Play Methods, Inverse Problems: Self-Calibration, Conditional Generation & Continuous Rep.

"Plug-and-Play Methods for Inverse Problems: Self-Calibration, Conditional Generation, and Continuous Representation"
Ulugbek Kamilov, Associate Professor of Electrical & Systems Engineering and Computer Science & Engineering, Washington University in St. Louis
Symposium overview: Generative AI: Diffusion Models for Scientific Machine Learning mini-symposium, co-organized with the Michigan Institute for Data Science and the department of Electrical and Computer Engineering at the University of Michigan.
Recently, diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image generation, audio synthesis, inverse problem solving, and many scientific disciplines. However, despite their impressive results, they also encounter numerous challenges and constraints that inhibit their practical implementation in many scientific pursuits. Therefore, this mini-symposium, co-organized by MIDAS and the Department of Electrical and Computer Engineering, will serve as a timely platform where experts and researchers from both methodology and application research fields will explore the latest progress and developments in generative AI and diffusion models, and delve into the application of these models in scientific and medical fields, which will become a prime venue for idea exchange and fostering research partnerships in this emerging field.
For more information about the series and speakers, please visit the event page at: midas.umich.edu/gen-ai-diffus...
- Sign up for the MIDAS newsletter: myumi.ch/rrxW3
- Twitter: @um_midas
- LinkedIn: / michigan-institute-for...
#datascience #artificialintelligence #research #ai #generativeai