YoVDO

Learning-Based Low-Rank Approximations - IPAM at UCLA

Offered By: Institute for Pure & Applied Mathematics (IPAM) via YouTube

Tags

Singular Value Decomposition Courses Deep Learning Courses Low-Rank Approximation Courses

Course Description

Overview

Explore a 49-minute lecture on learning-based algorithms for low-rank approximation problems presented by Piotr Indyk from the Massachusetts Institute of Technology. Delve into the recent advancements in optimizing algorithm performance using training sets of input matrices. Discover the two-step process of efficient approximate algorithms for computing low-rank approximations, involving the computation of a "sketch" and subsequent singular value decomposition. Learn how replacing random matrices with "learned" matrices significantly reduces approximation errors. Gain insights into joint work with Peter Bartlett, Yang Yuan, Ali Vakilian, Tal Wagner, and David Woodruff in this IPAM workshop on Multi-Modal Imaging with Deep Learning and Modeling.

Syllabus

Piotr Indyk - Learning-Based Low-Rank Approximations - IPAM at UCLA


Taught by

Institute for Pure & Applied Mathematics (IPAM)

Related Courses

Quantum-Inspired Classical Linear Algebra
Simons Institute via YouTube
Sampling for Linear Algebra, Statistics, and Optimization I
Simons Institute via YouTube
Foundations of Data Science II
Simons Institute via YouTube
Near Optimal Linear Algebra in the Online and Sliding Window Models
IEEE via YouTube
Low Rank Approximation in Electron Excitation Calculations - IPAM at UCLA
Institute for Pure & Applied Mathematics (IPAM) via YouTube