El Instituto de Ingeniería Matemática y Computacional (IMC) los saluda atentamente y los invita al seminario que se dictará esta semana.
Matthew Jacobs, Department of Mathematics, University of California, Santa Barbara.
Viernes 23 de agosto de 2024, 13:40 hrs. (Presencial en Sala de usos múltiples, piso 1, edificio Felipe Villanueva, Facultad de Matemáticas)
ABSTRACT
A particularly prominent area of machine learning is generative modeling, in which one seeks to artificially generate new data from a given input. For instance, generating images from a chosen text prompt. Mathematically, this can be posed as finding a map that pushes a simple probability distribution, such as a standard Gaussian to a complicated probability distribution. New data can then be created by sampling random points from the simple distribution and then sending them through the constructed map.
Currently, one of the most popular paradigms used in generative modeling is diffusion modeling, where the map is created by observing the behavior of a diffusion equation applied to the given data. In this talk, I will give an introduction to diffusion modeling, discuss some of my work approaching the problem from a deterministic PDE perspective (as opposed to the more typical stochastic approach), and mention some interesting open questions in this area.
