Skip to content. | Skip to navigation

Personal tools


UMR 5672

logo de l'ENS de Lyon
logo du CNRS
You are here: Home / Seminars / Machine Learning and Signal Processing / Learning with Low-rank Approximations

Learning with Low-rank Approximations

Jérémy Cohen (CNRS, IRISA, équipe PANAMA, Rennes)
When May 06, 2021
from 10:00 to 11:00
Attendees Jérémy Cohen
Add event to calendar vCal

Title : Learning with Low-rank Approximations

Asbtract : Matrix and tensor factorizations are widespread techniques to extract structure out of data in a potentially blind manner. Research on tensor methods is rapidly growing and encompases many aspect of computer science such as high performance computing and large scale non-convex optimization. An important challenge is to propose or study matrix and tensor models which are of practical interest while making efficient use of recent developements in both low-level tensor computation techniques such as tensor contractions on GPUs and large-scale non-convex optimization techniques such as stochastic gradient algorithms and proximal algorithms.  After introducing low-rank approximation methods and depicting the current research landscape, I will focus on two recent contributions: (i) Unsupervised music automatic segmentation using nonnegative Tucker decomposition (ii) Heuristic extrapolated block-coordinate descent algorithm for tensor decompositions.

Contact :

Séminaire en ligne : 

Room : webconf3.physique