Latest Articles for Pruning

Research introduces a systematic method for pruning large language models efficiently.

A new approach to machine translation evaluation metrics for better accessibility.

RankAdaptor optimizes fine-tuning for pruned AI models, enhancing performance efficiently.

FedMap improves Federated Learning efficiency while ensuring data privacy.

Combining pruning and quantization streamlines DNN efficiency for smaller devices.

An overview of how language models like Transformers operate and their significance.

A new method enhances B&B algorithms for L0-regularization problems.

A new method improves language models' efficiency while reducing costs and environmental impact.

Evaluating quantization and pruning to optimize DRL models for limited resources.

A method to enhance model efficiency in machine learning through effective pruning strategies.

LayerShuffle enhances the robustness of neural networks by enabling flexible layer execution.

A look into the safety concerns of compressed language models.

New methods reduce memory usage while maintaining performance in LLMs.

Research on nerve cell pruning offers insights into schizophrenia's development.

Techniques for optimizing RNNs, focusing on Mamba and quantization challenges.

This study explores methods to create smaller language models effectively and affordably.

This article analyzes model performance across various tasks and datasets.

New pruning techniques enhance deep learning models for smartphones with limited resources.

Methods to speed up speaker diarization without sacrificing accuracy.

This research studies the impact of pruning and random structures on RNN performance.

A new method enhances sparse language model training while minimizing performance loss.

A look at how neurons evolve during brain development.

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.