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SUMMARY:Diffusion Models Beyond Mean Prediction - Mingtian Zhang\, Univers
 ity College London
DTSTART:20250212T110000Z
DTEND:20250212T123000Z
UID:TALK228394@talks.cam.ac.uk
CONTACT:120952
DESCRIPTION: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 distributi
 on—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 exp
 ressive generative modeling.
LOCATION:Cambridge University Engineering Department\, CBL Seminar room BE
 4-38.
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