arXiv Machine Learning June 10, 2026 By Tudor Pistol

Learning Doubly Sparse Explicitly Conditioned Transforms

Read the original on arXiv Machine Learning →

arXiv:2606. 10975v1 Announce Type: new Abstract: Finding convenient spaces in which certain hypotheses regarding an assumed sparse structure of natural signals hold true has become a desirable result in recent research, its implications being reflected in areas such as data compression, noise reduction and feature extraction.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

benchmarks

arXiv Machine Learning

Jul 16

Multi-Dictionary Learning for Low Rank Sparse Coding

arXiv:2509. 10033v2 Announce Type: replace Abstract: Sparse dictionary coding represents signals as linear combinations of a few dictionary atoms.

By Boya Ma, Abram Magner, Maxwell McNeil, Petko Bogdanov

arXiv Machine Learning

5d ago

Sparse Orthogonal Regression Technique: A Spectral Framework for Equation Discovery, Approximation, and Integration

arXiv:2608. 13504v1 Announce Type: new Abstract: We develop the Sparse Orthogonal Regression Technique (SORT), a sparse spectral framework for learning orthonormal-basis expansions from noisy and irregularly sampled data.

By Sabin Roman, Ljupco Todorovski, Saso Dzeroski

Hugging Face Trending Papers

Jun 29

A Dual-domain Refinement Network with FBP-based Jacobian Learning for Sparse-view Dual-Energy CT Material Decomposition

Dual-energy CT (DECT) exploits attenuation differences across different X-ray spectra to provide richer material information and has been widely used in medical imaging. While sparse-view acquisition can lower radiation exposure, it makes DECT material decomposition even more challenging, as the problem is nonlinear and ill-posed.

efficiency

arXiv Machine Learning

Jun 9

Neural Legendre-Fenchel transform with Hessian Preconditioning

arXiv:2606. 09077v1 Announce Type: new Abstract: The Legendre-Fenchel (LF) transform is a fundamental tool in convex analysis and machine learning that maps lower semi-continuous functions to their convex conjugates.

By Basile Plus-Gourdon, Frank Nielsen

benchmarks

arXiv Machine Learning

Aug 5

Sparse Weight Decomposition for Efficient Circuit Extraction

arXiv:2608. 03913v1 Announce Type: new Abstract: Dense pretrained transformers do not naturally expose interpretable units for circuit extraction.

By Chuanhao Yan, Xuhan Huang, Yawen Duan, Zhenfei Yin, Hang Zhao, Bryan Dai, Jie Fu

llms fine-tuning benchmarks safety

arXiv Machine Learning

Jun 5

Learning solution operators of PDEs with sparse approximation methods

arXiv:2606. 06046v1 Announce Type: cross Abstract: We investigate the approximation of solution operators for partial differential equations (PDEs) using sparse high-dimensional techniques.

By Sebastian Neumayer, Daniel Potts, Fabian Taubert

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Multi-Dictionary Learning for Low Rank Sparse Coding

Sparse Orthogonal Regression Technique: A Spectral Framework for Equation Discovery, Approximation, and Integration

A Dual-domain Refinement Network with FBP-based Jacobian Learning for Sparse-view Dual-Energy CT Material Decomposition

Neural Legendre-Fenchel transform with Hessian Preconditioning

Sparse Weight Decomposition for Efficient Circuit Extraction

Learning solution operators of PDEs with sparse approximation methods