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.
Learning Doubly Sparse Explicitly Conditioned Transforms
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.
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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.
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.
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.
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.
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.
arXiv Machine Learning
Jun 10
One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data
arXiv:2606. 10678v1 Announce Type: new Abstract: Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture long-range dependencies.
arXiv Machine Learning
6d ago
A Variational Analysis of Kernel Learning with Learnable Linear Transformations
arXiv:2502. 11665v3 Announce Type: replace-cross Abstract: The classical kernel ridge regression problem aims to find the best fit for the output $Y$ as a function of the input data $X\in \mathbb{R}^d$, with a fixed choice of regularization term imposed by a given choice of a reproducing kernel Hilbert space, such as a Sobolev space.
arXiv Machine Learning
Jul 10
The Regularization Parameter: Sparse Precision Matrix Estimation
arXiv:2607. 07735v1 Announce Type: cross Abstract: Sparse precision matrix estimation provides an interpretable and computationally efficient framework for modeling conditional dependencies in high-dimensional, low-sample-size data.
arXiv AI
22h ago
Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors
arXiv:2608. 18036v1 Announce Type: cross Abstract: MRI reconstruction methods for undersampled k-space data naturally utilize complex-valued measurements.
arXiv Machine Learning
Jun 3
Correcting Neural Operator Spectral Bias via Diffusion Posterior Sampling with Sparse Observations
arXiv:2606. 03936v1 Announce Type: new Abstract: Neural operator surrogates (NO) approximate PDE solutions orders of magnitude faster than numerical solvers, but suffer from spectral bias: high-frequency content is systematically attenuated, limiting reliability where fine-scale structure matters.
arXiv AI
5d ago
Exploring Sparsity for Parameter Efficient Fine Tuning Using Wavelets for Vision
arXiv:2505. 12532v3 Announce Type: replace-cross Abstract: Efficiently adapting large pretrained models is critical under tight compute and memory budgets.