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arXiv · 2301.13584

Straight-Through meets Sparse Recovery: the Support Exploration Algorithm

Mimoun Mohamed·François Malgouyres·Valentin Emiya·Caroline Chaux
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Abstract

The {\it straight-through estimator} (STE) is commonly used to optimize quantized neural networks, yet its contexts of effective performance are still unclear despite empirical successes.To make a step forward in this comprehension, we apply STE to a well-understood problem: {\it sparse support recovery}. We introduce the {\it Support Exploration Algorithm} (SEA), a novel algorithm promoting sparsity, and we analyze its performance in support recovery (a.k.a. model selection) problems. SEA explores more supports than the state-of-the-art, leading to superior performance in experiments, especially when the columns of $A$ are strongly coherent.The theoretical analysis considers recovery guarantees when the linear measurements matrix $A$ satisfies the {\it Restricted Isometry Property} (RIP).The sufficient conditions of recovery are comparable but more stringent than those of the state-of-the-art in sparse support recovery. Their significance lies mainly in their applicability to an instance of the STE.

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Mimoun Mohamed, François Malgouyres, Valentin Emiya, Caroline Chaux. 2023-01-31. Straight-Through meets Sparse Recovery: the Support Exploration Algorithm. https://arxiv.org/abs/2301.13584

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