arXiv Machine Learning By Abhishek Tyagi, Arjun Iyer, Liam Young, William H Renninger, Christopher Kanan, Yuhao Zhu

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks

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arXiv:2510. 14812v2 Announce Type: replace Abstract: Structured weight sparsity accelerates training and inference on modern GPUs, but it trails unstructured dynamic sparse training (DST) in accuracy especially at extreme sparsity.

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arXiv Machine Learning
Aug 27

Deep greedy unfolding: Sorting out argsorting in greedy sparse recovery algorithms

The paper introduces Soft-OMP and Soft-IHT, permutation‑based variants of Orthogonal Matching Pursuit and Iterative Hard Thresholding that replace the non‑differentiable argsort with continuous soft‑sort operators. These differentiable algorithms enable the construction of fully trainable neural network architectures—OMP‑Net and IHT‑Net—while preserving the core greedy sparse recovery logic. The authors show both theoretically and numerically that the soft variants approximate their hard counterparts with controllable accuracy and can be extended to structured sparse recovery by learning structure‑aware weights.

By Sina Mohammad-Taheri, Matthew J. Colbrook, Simone Brugiapaglia