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.

翻译：直通估计器（STE）常被用于优化量化神经网络，尽管其实证效果显著，但其有效性能的具体情境仍不明确。为深化对此问题的理解，我们将STE应用于一个已被深入理解的问题：稀疏支撑恢复。本文提出一种新颖的促进稀疏性的算法——支撑探索算法（SEA），并分析了其在支撑恢复（亦称模型选择）问题中的性能。SEA能够探索比现有先进方法更多的支撑集，从而在实验中展现出更优越的性能，尤其在矩阵$A$的列向量具有强相干性时表现突出。理论分析在线性测量矩阵$A$满足限制等距性质（RIP）的条件下给出了恢复保证。恢复的充分条件与当前稀疏支撑恢复领域的最先进方法相当，但更为严格。这些条件的重要性主要在于它们适用于STE的一个具体实例。