University of Cambridge > Talks.cam > Machine Learning Reading Group @ CUED > Diffusion Models Beyond Mean Prediction

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹ️ Information

Diffusion Models Beyond Mean Prediction

Download to your calendar using vCal

If you have a question about this talk, please contact .

Teams link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.

Traditional diffusion models are typically trained to predict only the mean of the denoised distribution given a noisy sample. But what if we go beyond the mean? This talk explores how incorporating additional information—such as predicting the covariance of the denoised distribution—can significantly accelerate sampling and improve density estimation. We’ll dive into different techniques for covariance prediction, their theoretical connection, and practical benefits for more efficient and expressive generative modeling.

This talk is part of the Machine Learning Reading Group @ CUED series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Cambridge Carbon Nanotechnology Society Main Lecture Clinical School SR8, Addenbrookes Hospital

Other talks

Polar Oceans Seminar Talk Bacterial Physiology During Growth Arrest: Can It Be Targeted by Anti-Bacterial Therapeutics? Title to be confirmed Director's Briefing Reproducibility In Astrophysical Simulations: The AGORA Initiative and a 14-Year Human Experiment (In Memory of Joel Primack) Gastroenterology and Acute Medicine
 

© 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity