Latest Articles for Quantization

Explore innovative methods in global optimization using quantum mechanics.

Exploring the challenges and methods in detecting the elusive graviton particle.

DCTransformer enhances JPEG image quality lost during compression.

Augmented quantization improves data grouping and representation for better analysis.

Study explores FP8 formats for improved model efficiency and accuracy.

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

Learn how LoRA enables efficient fine-tuning of large models on consumer-grade hardware.

New methods enhance graph neural network efficiency with minimal performance loss.

MixQuant optimizes bit-width choices for deep neural networks, balancing efficiency and accuracy.

Exploring the potential of SNNs in edge computing applications.

A method for improved visualization of scattered data in particle dynamics.

A method to simplify CNNs during training while preserving performance.

An overview of remote state estimation and dynamic quantization methods.

This study examines methods to send data accurately amidst noise interference.

Learn how low-bit accumulators enhance DNN performance without sacrificing accuracy.

A look into how distributed optimization tackles large data challenges.

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

A new technique for optimizing large language models while maintaining performance.

Introducing ApiQ for improved fine-tuning and quantization of large language models.

This article discusses a new approach to improve text generation models using quantization.

A new approach to make language models smaller and faster using 1-bit quantization.

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

New quantization method enhances performance of large language models while reducing size.

New techniques enhance quantization while managing outliers for better model performance.

This article links classical models and quantum mechanics using mathematical structures.

Exploring model-free control techniques under limited communication channels.

A new method improves the efficiency of residual networks on FPGAs.

Exploring how neural networks can predict accurately on unseen data.

Investigating model compression methods to improve efficiency and defenses against attacks.

Exploring the balance between model compression and trustworthiness in AI.

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

D'OH offers new ways to represent signals efficiently.

This paper discusses the costs and improvements for low-precision neural networks.

Introducing a method to enhance image search using different model types.

Examining how quantization can improve neural network performance and generalization.

Evaluating accessible AI models for generating Python code with standard hardware.

New methods allow users to create game worlds using simple descriptions.

Improving federated learning with hierarchical structures and smart data handling.

This study evaluates how model size and quantization impact language model performance.

New techniques improve efficiency and accuracy in large language models.