用神经架构搜索自动发现信号处理中的稀疏恢复算法
Discovering Sparse Recovery Algorithms Using Neural Architecture Search
- 通过元学习与神经架构搜索，从超5万变量空间中自动发现算法结构
- 成功复现了ISTA和FISTA算法的核心组件，验证了框架的有效性
- 方法可推广至多种数据分布和不同逆问题，适用于算法自动化设计
信号处理中逆问题的新型算法设计是一项极其困难、依赖经验且耗时的任务。本文提出利用元学习工具（如神经架构搜索，NAS）在信号处理场景中实现算法的自动化发现。具体地，以迭代收缩阈值算法（ISTA）及其加速版本快速ISTA（FISTA）为候选目标，构建了一个元学习框架。该框架在包含超过5万个变量的搜索空间中，成功复现了上述两种算法的关键元素。进一步实验表明，该方法可拓展至多种数据分布及其他算法，具备良好的通用性和应用潜力。
原文摘要 · Abstract (English)
The design of novel algorithms for solving inverse problems in signal processing is an incredibly difficult, heuristic-driven, and time-consuming task. In this short paper, we the idea of automated algorithm discovery in the signal processing context through meta-learning tools such as Neural Architecture Search (NAS). Specifically, we examine the Iterative Shrinkage Thresholding Algorithm (ISTA) and its accelerated Fast ISTA (FISTA) variant as candidates for algorithm rediscovery. We develop a meta-learning framework which is capable of rediscovering (several key elements of) the two aforementioned algorithms when given a search space of over 50,000 variables. We then show how our framework can apply to various data distributions and algorithms besides ISTA/FISTA.
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