1.8k citations · 1.9k across the 8 of their papers we have counts for
8 papers
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning
Thanveer Shaik, Xiaohui Tao, Lin Li +4
Machine Unlearning is an emerging field that addresses data privacy issues by enabling the removal of private or irrelevant data from the Machine Learning process. Challenges relat…
Norm Tweaking: High-performance Low-bit Quantization of Large Language Models
Liang Li, Qingyuan Li, Bo Zhang +1
As the size of large language models (LLMs) continues to grow, model compression without sacrificing accuracy has become a crucial challenge for deployment. While some quantization…
FPTQ: Fine-grained Post-Training Quantization for Large Language Models
Qingyuan Li, Yifan Zhang, Liang Li +6
In the era of large-scale language models, the substantial parameter size poses significant challenges for deployment. Being a prevalent compression technique, quantization has eme…
Results of 23 yr of Pulsar Timing of PSR J1453-6413
Wei Li, Shi-Jun Dang, Jian-Ping Yuan +20
In this paper, we presented the 23.3 years of pulsar timing results of PSR J1456-6413 based on the observation of Parkes 64m radio telescope. We detected two new glitches at MJD 57…
Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy
Thanveer Shaik, Xiaohui Tao, Haoran Xie +3
Machine unlearning (MU) is gaining increasing attention due to the need to remove or modify predictions made by machine learning (ML) models. While training models have become more…
EAPruning: Evolutionary Pruning for Vision Transformers and CNNs
Qingyuan Li, Bo Zhang, Xiangxiang Chu
Structured pruning greatly eases the deployment of large neural networks in resource-constrained environments. However, current methods either involve strong domain expertise, requ…
YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
Chuyi Li, Lulu Li, Hongliang Jiang +15
For years, the YOLO series has been the de facto industry-level standard for efficient object detection. The YOLO community has prospered overwhelmingly to enrich its use in a mult…
Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search
Xiangxiang Chu, Bo Zhang, Hailong Ma +2
Deep convolutional neural networks demonstrate impressive results in the super-resolution domain. A series of studies concentrate on improving peak signal noise ratio (PSNR) by usi…