Latest Articles for Model Optimization

WGQA enhances the efficiency of language models while reducing memory needs.

LIAR offers a new way to prune models without retraining, enhancing efficiency and performance.

New framework improves knowledge distillation by focusing on hard samples.

DDK enhances knowledge distillation, making smaller language models more efficient.

SINDER enhances Vision Transformers by addressing image analysis defects.

A new framework enhances diffusion models' efficiency while preserving image quality.

A new method improves accuracy in quantizing Vision Transformers without original data.

MoFO helps large language models retain knowledge during fine-tuning without losing performance.

A look into how diffusion models generate data and their practical uses.

A new method enhances architecture search for deep learning models.

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

A new method improves multi-task learning in language models by sharing knowledge.

A new framework called CoRa improves model performance during low-bit quantization.

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

Eigen Attention improves memory efficiency for large language models processing long texts.

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

A new method improves performance of Vision Transformers through effective token compression.

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

BAM enhances MoE efficiency by integrating attention and FFN parameters.

Techniques to reduce model size for effective deployment in limited-resource environments.

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

Using Transformers to enhance State-Space Models for better efficiency in NLP.

Discover strategies to enhance few-shot learning in large vision language models.

A new approach to merge machine learning models based on user preferences for better outcomes.

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

A new approach to enhance decision tree models in reinforcement learning.

Introducing FISTAPruner, a method to prune language models efficiently while keeping performance high.

This article explores a new method for better merging of machine learning models.

LLaMA3-70B faces unique issues with 8-bit quantization affecting its performance.

Combine trained models to improve performance and reduce costs.

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

Learn how new methods enhance weight-ensembling in machine learning.

RoLoRA enhances federated learning with robust fine-tuning and efficient communication.

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

RPP improves fitting and generalization in Vision-Language Models using refined prompts.

A new method enhances model performance while ensuring privacy in deep learning.

ETAGE improves model performance during testing with new data types.

Examining how flexibility in models enhances predictive accuracy through dynamic adjustments.

A new technique cuts memory needs for large language models while keeping performance.

Improving model efficiency in remote sensing through knowledge distillation techniques.