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Abstract: Approximating a matrix by a product of few sparse factors whose supports possess the butterfly structure, which is common to many fast transforms, is key to learn fast transforms and speed up algorithms for inverse problems.
Approximating a matrix by a product of few sparse factors whose supports possess the butterfly structure, which is common to many fast transforms, ...
Feb 15, 2022 · ABSTRACT. Approximating a matrix by a product of few sparse factors whose supports possess the butterfly structure, which is com-.
Our main contribution is an algorithmic framework to ad- dress Problem (1) when the fixed supports of the J ≥ 2 factors have the butterfly structure [6, 4].
FAST LEARNING OF FAST TRANSFORMS, WITH GUARANTEES. QUOC TUNG LE, LÉON ZHENG, ELISA RICCIETTI, RÉMI GRIBONVAL. Université de Lyon, ENS de Lyon, CNRS, INRIA ...
Fast learning of fast transforms, with guarantees. Published in IEEE International Conference on Acoustics, Speech and Signal Processing, May 2022, Singapore ...
We investigate how reinforcement learning can be used to train level-designing agents. This represents a new approach to procedural content generation in games, ...
May 6, 2022 · Experiments show that speed and accuracy of the factorization can be jointly improved by several orders of magnitude, compared to gradient-based ...
Code to reproduce experiments in "Fast learning of fast transforms, with guarantees" (Quoc-Tung Le, Léon Zheng, Elisa Riccietti, Rémi Gribonval).
Fast learning of fast transforms, with guarantees. IEEE ICASSP 2022, Singapore. Quoc-Tung Le, Léon Zheng, Elisa Riccietti, Rémi Gribonval. May, 2022. Le, Zheng ...
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