Average of Pruning: Improving Performance and Stability of Out-of-Distribution Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Detecting Out-of-distribution (OOD) inputs have been a critical issue for neural networks in the open world. However, the unstable behavior of OOD detection along the optimization trajectory during training has not been explored clearly. In this paper, we first find the performance of OOD detection suffers from overfitting and instability during training: 1) the performance could decrease when the training error is near zero, and 2) the performance would vary sharply in the final stage of training. Based on our findings, we propose Average of Pruning (AoP), consisting of model averaging and pruning, to mitigate the unstable behaviors. Specifically, model averaging can help achieve a stable performance by smoothing the landscape, and pruning is certified to eliminate the overfitting by eliminating redundant features. Comprehensive experiments on various datasets and architectures are conducted to verify the effectiveness of our method.
Blind Adversarial Pruning: Balance Accuracy, Efficiency and Robustness
Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology（中国空间技术研究院钱学森空间技术实验室）；School of Mathematics and Statistics, Wuhan University（武汉大学数学与统计学院）
One-Cycle Structured Pruning via Stability-Driven Subnetwork Search
IT Application Research Center, Korea Electronics Technology Institute（韩国电子技术研究所应用信息技术中心）；Department of Intelligent Semiconductors, Soongsil University（顺慈大学智能半导体系）；Department of Computer Science & Engineering, Seoul National University（首尔国立大学计算机科学与工程系）
NTK-SAP: Improving neural network pruning by aligning training dynamics
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Shenzhen International Center for Industrial and Applied Mathematics（深圳国际工业与应用数学中心）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning
National Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）
Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI
Northeastern University（东北大学）；North Dakota State University（北达科他州立大学）；Washington State University（华盛顿州立大学）；University of Connecticut（康涅狄格大学）；Chongqing University（重庆大学）；University of South Alabama（南阿拉巴马大学）
College of Computer（计算机学院）；National University of Defense Technology（国防科技大学）；School of Computer and Communication Engineering（计算机与通信工程学院）；Changsha University of Science and Technology（长沙理工大学）；Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transp（湖南省大数据智能处理重点实验室）
Lightweight Intrusion Detection in IoT via SHAP-Guided Feature Pruning and Knowledge-Distilled Kronecker Networks
LSIA Laboratory, Moroccan School of Engineering Sciences (EMSI)（摩洛哥工程科学学校LSIA实验室）；Laboratory of Research in Informatics (LaRI), Faculty of Sciences, Ibn Tofail University（伊本·泰菲勒大学信息研究实验室）；College of Computing, University Mohammed VI Polytechnic（穆莱·伊沙克六世理工学院计算机学院）
PARIS: Pruning Algorithm via the Representer theorem for Imbalanced Scenarios
Department of Physics and Astronomy（物理与天文学系）；Queen Mary University of London（伦敦女王玛丽大学）；University of Colorado（科罗拉多大学）；SWx-TREC (Space Weather Technology Research and Education Center)（空间天气技术研究与教育中心）
Beware of Calibration Data for Pruning Large Language Models
School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）；Key Laboratory of Data Intelligence and Advanced Computing, Soochow University（苏州大学数据智能与先进计算重点实验室）；Huawei Cloud, China（华为云（中国））
