# Straight-Through meets Sparse Recovery: the Support Exploration Algorithm

arXiv: https://arxiv.org/abs/2301.13584 · HTML: https://mlsanity.com/paper/2301.13584/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 31 Jan 2023.

## Builds on (most cited first, 13 of 13)

- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://mlsanity.com/paper/1511.00363/) (2015)
- [Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets](https://mlsanity.com/paper/1903.05662/) (2019)
- [Training Quantized Nets: A Deeper Understanding](https://mlsanity.com/paper/1706.02379/) (2017)
- [Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization](https://mlsanity.com/paper/1906.02107/) (2019)
- [A comprehensive review of Binary Neural Network](https://mlsanity.com/paper/2110.06804/) (2021)
- [Exact Support Recovery for Sparse Spikes Deconvolution](https://mlsanity.com/paper/1306.6909/) (2013)
- [The High-Dimensional Geometry of Binary Neural Networks](https://mlsanity.com/paper/1705.07199/) (2017)
- [Geometry and Symmetry in Short-and-Sparse Deconvolution](https://mlsanity.com/paper/1901.00256/) (2019)
- [Sparse Convex Optimization via Adaptively Regularized Hard Thresholding](https://mlsanity.com/paper/2006.14571/) (2020)
- [Learning Quantized Neural Nets by Coarse Gradient Method for Non-linear Classification](https://mlsanity.com/paper/2011.11256/) (2020)
- [Straight-Through Estimator as Projected Wasserstein Gradient Flow](https://mlsanity.com/paper/1910.02176/) (2019)
- [Deconvolution of Point Sources: A Sampling Theorem and Robustness Guarantees](https://mlsanity.com/paper/1707.00808/) (2017)

## Built on by (most cited first, 2 of 2)

- [High-Dimensional Learning Dynamics of Quantized Models with Straight-Through Estimator](https://mlsanity.com/paper/2510.10693/) (2025)
- [Beyond Discreteness: Sample Complexity Analysis of Straight-Through Estimator for 1-bit Quantization](https://mlsanity.com/paper/2505.18113/) (2025)

## Abstract (arXiv)

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.