Latest Articles for Quantization

A framework for safer data processing in machine learning.

This article discusses effective gradient estimators for quantization-aware training in deep learning.

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

Learn effective methods to quantize LLMs while maintaining accuracy and performance.

This study investigates memory efficiency in large language models through low-rank decomposition.

Combining SmoothQuant and GPTQ improves efficiency and performance of large language models.

Examining the weaknesses of DNNs against adversarial examples and their implications.

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

PV-Tuning improves fine-tuning and compression for large language models.

New methods improve model performance during quantization in language models.

New techniques enable training large neural networks on consumer-grade hardware with reduced memory.

Examining the dangers of quantized language models and their potential misuse.

Learn how HGQ optimizes deep learning models for speed and accuracy.

A new method for running Diffusion Transformers more effectively on smaller devices.

Research on optimizing deep learning models with sparsity and quantization techniques.

Examining the impact of calibration set quality on LLM performance post-quantization.

A new method improves data privacy for discrete data analysis.

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

A method to enhance decision-making in reinforcement learning using representation learning.

A new method improves image and video generation speed and quality.

Research on quantization techniques for efficient data transmission in noisy channels.

Introducing a method to fine-tune LLMs on low-resource devices.

Tender offers a novel solution for efficient use of large language models.

Explore methods for compressing images while saving energy without sacrificing quality.

A study on the decision-making processes of large language models.

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

QuEE combines quantization and early exiting for efficient machine learning.

This article presents a method to protect personal data in machine learning systems.

BrightFit enhances course suggestions through a new two-stage retrieval approach.

Evaluating methods to enhance long context performance in language models.

Advancements in predicting speech quality using efficient methods for mobile devices.

A method to convert continuous data into a simpler, discrete form.

Combining pruning and quantization streamlines DNN efficiency for smaller devices.

Examining quantization techniques for better distributed learning across various network structures.

This article explores the benefits of using FP8 in federated learning.

Studying how quantization affects performance in different languages.

GPTQT enhances efficiency and performance in large language model quantization, making AI more accessible.

This paper presents a method to identify and manage harmful memes effectively.

ISQuant offers a new approach to quantization for efficient model deployment.

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