Sparsifying Parametric Models with L0 Regularization

Nicolò Botteghi; Urban Fasel

doi:10.48550/arXiv.2409.03489

Sparsifying Parametric Models with L0 Regularization

Nicolò Botteghi, Urban Fasel

Mathematics of Imaging & AI

Research output: Working paper › Preprint › Academic

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Abstract

This document contains an educational introduction to the problem of sparsifying parametric models with L0 regularization. We utilize this approach together with dictionary learning to learn sparse polynomial policies for deep reinforcement learning to control parametric partial differential equations. The code and a tutorial are provided here: https://github.com/nicob15/Sparsifying-Parametric-Models-with-L0.

Original language	English
Publisher	ArXiv.org
DOIs	https://doi.org/10.48550/arXiv.2409.03489
Publication status	Published - 5 Sept 2024

Keywords

cs.LG

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10.48550/arXiv.2409.03489

2409.03489v1Final published version, 0.99 MB

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AB - This document contains an educational introduction to the problem of sparsifying parametric models with L0 regularization. We utilize this approach together with dictionary learning to learn sparse polynomial policies for deep reinforcement learning to control parametric partial differential equations. The code and a tutorial are provided here: https://github.com/nicob15/Sparsifying-Parametric-Models-with-L0.

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Sparsifying Parametric Models with L0 Regularization

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Keywords

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