arXiv Machine Learning By Geoffrey Kasenbacher, Daniel Ruepp, Gerrit A. Ecke

Convolutional Sparse Coding via the Locally Competitive Algorithm on Loihi 2

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arXiv:2606. 08584v1 Announce Type: new Abstract: Sparse coding provides a principled framework for signal representation by expressing an input as a linear combination of only a small number of basis functions.

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arXiv Machine Learning
Jun 30

SparsePixels: Efficient Convolution for Sparse Data on FPGAs

arXiv:2512. 06208v3 Announce Type: replace-cross Abstract: Inference of standard convolutional neural networks (CNNs) on FPGAs often incurs high latency and a long initiation interval due to the deep nested loops required to densely convolve every input pixel regardless of its feature value.

By Ho Fung Tsoi, Dylan Rankin, Vladimir Loncar, Philip Harris
arXiv Computer Vision
Sep 18

Training-Adaptive Convolutional Sparse Coding via Information Bottleneck for Robust Visual Representation

The paper introduces a training‑adaptive convolutional sparse coding (CSC) framework that learns the sparsity coefficient jointly with network parameters using an unfolded FISTA optimization. By treating the coefficient as a differentiable variable, the method balances information retention and compression through an information bottleneck perspective, promoting compact yet task‑relevant representations. A label‑free post‑training strategy further adjusts compression for corrupted inputs, yielding competitive accuracy on clean data and enhanced robustness to perturbations on CIFAR and ImageNet.

By Meng'en Qin, Yinchen Liu, Mingxuan Cui, Youlu Xing