Some exactly solvable models for machine learning via Statistical physics
https://youtu.be/uUUeTYzMu0Q Speaker: Florent Krzakala, EPFL Title: Some exactly solvable models for machine learning via Statistical physics Abstract: The increasing dimensionality of data in the modern machine learning age presents new challenges and opportunities. The high dimensional settings allow one to use powerful asymptotic methods from probability theory and statistical physics to obtain precise characterizations and […]
Towards AI for mathematical modeling of complex biological systems: Machine-learned model reduction, spatial graph dynamics, and symbolic mathematics
https://youtu.be/t4xRwWxTzSg Speaker: Eric Mjolsness, Departments of Computer Science and Mathematics, UC Irvine Title: Towards AI for mathematical modeling of complex biological systems: Machine-learned model reduction, spatial graph dynamics, and symbolic mathematics Abstract: The complexity of biological systems (among others) makes demands on the complexity of the mathematical modeling enterprise that could be satisfied with mathematical […]
CMSA Math-Science Literature Lecture: Knot Invariants From Gauge Theory in Three, Four, and Five Dimensions
Edward Witten (IAS) Title: Knot Invariants From Gauge Theory in Three, Four, and Five Dimensions Abstract: I will explain connections between a sequence of theories in two, three, four, and five dimensions and describe how these theories are related to the Jones polynomial of a knot and its categorification. Talk chair: Cliff Taubes Video