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20232025
most citedDeep learning for state estimation of commercial sodium-ion batteries using partial charging profiles: validation with a multi-temperature ageing dataset

5 citations · 6 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.LG2025★ 5 cited

Deep learning for state estimation of commercial sodium-ion batteries using partial charging profiles: validation with a multi-temperature ageing dataset

Jiapeng Liu, Lunte Li, Jing Xiang +3

Accurately predicting the state of health for sodium-ion batteries is crucial for managing battery modules, playing a vital role in ensuring operational safety. However, highly acc…

cs.LG2025

Hyperbolic Binary Neural Network

Jun Chen, Jingyang Xiang, Tianxin Huang +2

Binary Neural Network (BNN) converts full-precision weights and activations into their extreme 1-bit counterparts, making it particularly suitable for deployment on lightweight mob…

cs.LG2024

DFRot: Achieving Outlier-Free and Massive Activation-Free for Rotated LLMs with Refined Rotation

Jingyang Xiang, Sai Qian Zhang

Rotating the activation and weight matrices to reduce the influence of outliers in large language models (LLMs) has recently attracted significant attention, particularly in the co…

cs.LG2024★ 1 cited

QQQ: Quality Quattuor-Bit Quantization for Large Language Models

Ying Zhang, Peng Zhang, Mincong Huang +7

Quantization is a proven effective method for compressing large language models. Although popular techniques like W8A8 and W4A16 effectively maintain model performance, they often…

cs.LG2024

AutoDFP: Automatic Data-Free Pruning via Channel Similarity Reconstruction

Siqi Li, Jun Chen, Jingyang Xiang +2

Structured pruning methods are developed to bridge the gap between the massive scale of neural networks and the limited hardware resources. Most current structured pruning methods…

cs.LG2023

SUBP: Soft Uniform Block Pruning for 1xN Sparse CNNs Multithreading Acceleration

Jingyang Xiang, Siqi Li, Jun Chen +4

The study of sparsity in Convolutional Neural Networks (CNNs) has become widespread to compress and accelerate models in environments with limited resources. By constraining N cons…