Generative AI
Research in Motion: Nicole Immorlica (Microsoft Research) “The Economic Impacts of Generative AI”
Co-sponsored by CADMY, FDS, and Tsai CITY Speaker bio: Nicole Immorlica is a senior principal researcher at Microsoft Research New England (MSR NE) where […]
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 […]
S&DS Seminar: Stan Osher (UCLA), “Recent Results on Mean Field Games, Optimal Transport, and In-Context Learning”
Abstract: We have recently been developing algorithms related to mean field games, optimal transport, in-context learning, score based generative models and links between Laplace’s method, […]
FDS Colloquium: Song Mei (Berkeley), “Revisiting neural network approximation theory in the age of generative AI”
Optional Zoom link: https://yale.zoom.us/j/97222935172 Abstract: Textbooks on deep learning theory primarily perceive neural networks as universal function approximators. While this classical viewpoint is fundamental, it […]
