Latest Articles for Pruning

Making vision transformers efficient for drones and mobile devices to enhance visual tasks.

New neural networks designed for better performance in resource-limited devices.

CERP optimizes memory use in recommendation systems without losing accuracy.

Streamlined models can enhance healthcare outcomes using machine learning.

Research reveals insights into blood vessel growth and its importance for brain health.

This study reviews efficient neural network techniques for image analysis in remote sensing.

A new method for optimizing DNNs for resource-limited devices.

Research enhances deep learning models for resource-constrained environments.

ThinResNet establishes reliable benchmarks for comparing pruning methods in neural networks.

Research highlights methods to compress language models while preserving performance in code generation.

New method reduces vision tokens for cost-effective training.

New pruning methods enhance zero-shot multi-speaker text-to-speech model performance.

Bayesian sparsification streamlines deep learning models for better efficiency and performance.

A method to simplify CNNs during training while preserving performance.

A new method that improves pruned neural networks for better performance.

This article reviews techniques to enhance Large Language Models' efficiency and performance.

Intra-Fusion combines neurons, making neural networks smaller and more efficient.

SLEB streamlines LLMs by removing redundant transformer blocks, enhancing speed and efficiency.

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

A new approach enhances the detection of unseen data in machine learning models.

This study examines how model compression impacts speech recognition in noisy environments.

A new method streamlines pruning of neural networks for improved performance.

Researchers simplify neural networks to boost performance on limited-resource devices.

Exploring the balance between model compression and trustworthiness in AI.

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

This article discusses pruning methods to enhance neural network efficiency.

ALoRA enhances language model tuning by dynamically allocating parameters.

New methods improve efficiency of deep neural networks for limited-resource devices.

A new approach to reduce CNN complexity while maintaining performance.

Research enhances robotic pruning accuracy using simulation and deep learning techniques.

Introducing a method for task-agnostic pruning of complex models.

COPAL enhances language models for better adaptation without retraining.

A new system improves efficiency in analyzing graph data patterns.

Explore methods to enhance efficiency and security of deep neural networks.

A new method for compressing CNNs while maintaining accuracy for efficient image processing.

A clear guide on decision trees and their real-world applications.

Improved strategies for efficient job scheduling on parallel machines.

DSNNs process information like real neurons, offering improved efficiency for data handling.

New method enhances image restoration by reducing noise and preserving details.

VTrans method significantly reduces transformer model sizes without sacrificing performance.