Latest Articles for Pruning

Learn methods to optimize large language models for better performance and efficiency.

Learn how pruning helps reduce neural network size while maintaining performance.

Research reveals how to make speech models smaller and more efficient.

Learn how CON-FOLD improves understanding of machine learning decisions.

Learn how PQV-Mobile enhances ViTs for efficient mobile applications.

Examining defensive methods to secure Federated Learning from data breaches.

Critical periods shape sensory processing and neural circuits in the brain.

A new technique enhances the efficiency of pre-trained language models.

A method to shrink language models without sacrificing effectiveness through pruning and distillation.

Learn how model pruning enhances AI performance and reduces resource needs.

A fresh method reduces neural network size while preserving performance.

A new method using AutoSparse for efficient neural network pruning at the start.

Researching hardware designs to optimize CNNs for energy-efficient image processing.

A new method improves DNN security without clean data.

An innovative approach to compress advanced models efficiently without losing performance.

Examining new methods to enhance neural network efficiency and security.

Learn how model compression improves efficiency of large language models.

New methods improve neural network performance on limited-resource devices.

A novel approach to create decision trees for complex systems without prior knowledge.

This article discusses the benefits of simplifying transformer models for speech tasks.

Introducing FedFT, a method to improve communication in Federated Learning.

HESSO simplifies model compression, making neural networks more efficient without losing performance.

A new approach speeds up processing in large language models for better performance.

Exploring a novel proof for the B-series composition theorem using unlabelled trees.

Personalized systems enhance monitoring of health and behavior through adjusted models.

Examining how SSL models memorize data points and its implications.

Analyzing the effects of pruning methods on GoogLeNet's performance and interpretability.

Innovative methods aim to make large language models more efficient and deployable.

Innovative methods for improving neural networks with less computing power.

MicroScopiQ improves AI models' performance while consuming less energy.

QuanCrypt-FL enhances security in Federated Learning using advanced techniques.

A new approach improves clarity in computer language models.

ZipNN compresses AI models efficiently, keeping essential details intact.

AI tools like ChatGPT face energy efficiency challenges that need solutions.

Keeping AI conversations safe on the go with Llama Guard.

A new method improves efficiency in machine learning data processing.

A new method to optimize large language models efficiently.

Discover how EAST optimizes deep neural networks through effective pruning methods.

Research shows how to compress diffusion models while maintaining quality.

A new method leverages gravity concepts to effectively prune deep convolutional neural networks.