Sampling
S&DS Colloquium: Thuy-Duong “June” Vuong (Miller Institute, Berkeley), “Efficiently learning and sampling from multimodal distributions using data-based initialization”
Abstract: Learning to sample is a central task in generative AI: the goal is to generate (infinitely many more) samples from a target distribution $\mu$ […]
FDS x Applied Physics Colloquium: Grant Rotskoff (Stanford), “Efficient variational inference with generative models”
Abstract: Neural networks continue to surprise us with their remarkable capabilities for high-dimensional function approximation. Applications of machine learning now pervade essentially every scientific […]
FDS Conference: Recent Advances and Future Directions for Sampling
Hosted by The Yale Institute for Foundations of Data Science. Please join our mailing list for future announcements.
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