sparse-learning

Modular and scalable computational imaging in Python with GPU/out-of-core computing.

Sparse Switchable Normalization with sparse activation function SparestMax

Unsupervised Feature Selection based on Adaptive Similarity Learning and Subspace Clustering

Low-rank Dictionary Learning for Unsupervised Feature Selection

A Pytorch library for stochastic variational inference with sparsity inducing priors.

This repo is an educational POC showing how thermodynamic sampling units can produce sparse polynomial approximations. It’s intended for demonstration, not production.

QSELM: 34.1M CPU LM, 215,771 training tok/s (8,529x measured Qwen training); sealed QA 90.6% vs Qwen3.5-0.8B 45.8%, memory 69.6% vs Qwen3-0.6B-FC 3.2%. No GPU. | QSELM：3408.7万参数CPU模型，训练21.6万token/s（Qwen训练实测的8,529倍）；封存问答90.6%对Qwen3.5-0.8B 45.8%，跨轮记忆69.6%对Qwen3-0.6B-FC 3.2%；无需GPU。

SG-XDEAT: Sparsity-Guided Cross-Dimensional and Cross-Encoding Attention with Target-Aware Conditioning in Tabular Learning