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Functional Gradient Descent Methods for Optimization and Sampling

Offered By: VinAI via YouTube

Tags

Gradient Descent Courses Sampling Courses Constrained Optimization Courses Neural Architecture Search Courses

Course Description

Overview

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Explore advanced machine learning concepts in this seminar on Functional Gradient Descent methods for optimization and sampling. Delve into the research of Dr. Qiang Liu, Assistant Professor of Computer Science at the University of Texas at Austin, as he discusses innovative applications of gradient descent beyond traditional Euclidean spaces. Learn about Stein variational gradient descent and its use in drawing samples from complex distributions. Discover how splitting steepest neural architecture descent can be applied to jointly estimate neural network weights and structures. Examine extensions of these techniques for handling constrained, bilevel, and multi-objective optimization problems. Gain insights into cutting-edge approaches that push the boundaries of statistical learning methods for high-dimensional and complex data.

Syllabus

Seminar Series: Functional Gradient Descent methods for optimization and sampling


Taught by

VinAI

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