Latest Articles for Sparsity

This paper examines error bounds in hierarchical Bayesian models for high-dimensional parameters.

New methods improve efficiency and flexibility in optimal transport using tempered exponential measures.

This article discusses the SpongeNet Attack and its impact on energy consumption in deep learning models.

Learn how new pruning methods enhance efficiency in deep neural networks without sacrificing accuracy.

ProSparse improves activation sparsity in LLMs for better efficiency and performance.

Masked Matrix Multiplication improves efficiency in AI computations by utilizing data sparsity.

A new method enhances PSD estimation accuracy in weather radar systems.

NeuroPrune shrinks language models while maintaining performance, making them faster and more accessible.

New algorithms enhance privacy and accuracy in sparse data scenarios.

A look at the linear model of coregionalization for analyzing spatial data.

Examining how network width impacts knowledge retention during sequential learning tasks.

New methods improve deep learning efficiency on structured hardware.

A new technique enhances efficiency in spectral compressed sensing.

New methods enhance efficiency in recommendation systems by addressing data sparsity.

A novel pruning method enhances efficiency of deep learning in crossbar systems.

A comprehensive method for selecting effective counterfactuals in machine learning models.

New methods enhance predictions of complex systems using data-driven reduced-order models.

A novel approach to enhance gradient-based saliency maps for better model interpretation.

This work examines effective sparse optimization strategies in fractional Sobolev spaces.

A study on dividing sparse graphs into two trees under specific rules.

Examining key aspects of measures in optimization with a focus on optimality conditions.

A new method enhances the study of turbulent flows through localized analysis.

Introducing a new method for efficient training of structured neural networks.

A new model reveals how deep networks learn efficiently from structured, sparse data.

Discover a powerful solver for tackling complex nonlinear equations in various fields.

A new method enhances clarity and performance of GNN predictions.

New method smup improves efficiency in training sparse neural networks.

CAVIAR transforms how categorical variables are analyzed, improving outcomes in various fields.

Research on optimizing deep learning models with sparsity and quantization techniques.

SpaFL enhances communication and computation in Federated Learning while securing personal data.

Leveraging external data for improved feature selection in genomic studies.

This approach enhances solution finding in sparse problems.

New methods promise faster, efficient neural networks with less resource use.

Introducing a method to fine-tune LLMs on low-resource devices.

This study presents improved methods for identifying galaxy cluster states using simulations.

Exploring limited feedback in machine learning classification tasks.

A new method improves sampling from high-dimensional data using reinforcement learning.

This study focuses on hidden symmetries in high-dimensional linear bandits.

A new method enhances covariance estimation in Gaussian processes with adaptive thresholding.

A new method improves identification of switched linear systems through robust techniques.