IEEE transactions on neural networks and learning systems最新文献_第4页

Knowledge-Guided Label Distribution Calibration for Federated Affective Computing 面向联邦情感计算的知识导向标签分布校准

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-21 DOI: 10.1109/tnnls.2025.3568458

Zixin Zhang, Fan Qi, Changsheng Xu

引用次数: 0

UBG: An Unreal BattleGround Benchmark With Object-Aware Hierarchical Proximal Policy Optimization. UBG：具有对象感知分层最接近策略优化的虚幻战场基准。

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-20 DOI: 10.1109/tnnls.2025.3567001

Longyu Niu,Baihui Li,Xingjian Fan,Hao Fang,Jun Li,Junliang Xing,Jun Wan,Zhen Lei

{"title":"UBG: An Unreal BattleGround Benchmark With Object-Aware Hierarchical Proximal Policy Optimization.","authors":"Longyu Niu,Baihui Li,Xingjian Fan,Hao Fang,Jun Li,Junliang Xing,Jun Wan,Zhen Lei","doi":"10.1109/tnnls.2025.3567001","DOIUrl":"https://doi.org/10.1109/tnnls.2025.3567001","url":null,"abstract":"The deep reinforcement learning (DRL) has made significant progress in various simulation environments. However, applying DRL methods to real-world scenarios poses certain challenges due to limitations in visual fidelity, scene complexity, and task diversity within existing environments. To address limitations and explore the potential ability of DRL, we developed a 3-D open-world first-person shooter (FPS) game called Unreal BattleGround (UBG) using the unreal engine (UE). UBG provides a realistic 3-D environment with variable complexity, random scenes, diverse tasks, and multiple scene interaction methods. This benchmark involves far more complex state-action spaces than classic pseudo-3-D FPS games (e.g., ViZDoom), making it challenging for DRL to learn human-level decision sequences. Then, we propose the object-aware hierarchically proximal policy optimization (OaH-PPO) method in the UBG. It involves a two-level hierarchy, where the high-level controller is tasked with learning option control, and the low-level workers focus on mastering subtasks. To boost the learning of subtasks, we propose three modules: an object-aware module for extracting depth detection information from the environment, potential-based intrinsic reward shaping for efficient exploration, and annealing imitation learning (IL) to guide the initialization. Experimental results have demonstrated the broad applicability of the UBG and the effectiveness of the OaH-PPO. We will release the code of the UBG and OaH-PPO after publication.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"11 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144103752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning Self-Growth Maps for Fast and Accurate Imbalanced Streaming Data Clustering. 学习自成长图用于快速准确的不平衡流数据聚类。

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-20 DOI: 10.1109/tnnls.2025.3563769

Yiqun Zhang,Sen Feng,Pengkai Wang,Zexi Tan,Xiaopeng Luo,Yuzhu Ji,Rong Zou,Yiu-Ming Cheung

{"title":"Learning Self-Growth Maps for Fast and Accurate Imbalanced Streaming Data Clustering.","authors":"Yiqun Zhang,Sen Feng,Pengkai Wang,Zexi Tan,Xiaopeng Luo,Yuzhu Ji,Rong Zou,Yiu-Ming Cheung","doi":"10.1109/tnnls.2025.3563769","DOIUrl":"https://doi.org/10.1109/tnnls.2025.3563769","url":null,"abstract":"Streaming data clustering is a popular research topic in data mining and machine learning. Since streaming data is usually analyzed in data chunks, it is more susceptible to encountering the dynamic cluster imbalance issue. That is, the imbalance ratio (IR) of clusters changes over time, which can easily lead to fluctuations in either the accuracy or the efficiency of streaming data clustering. Therefore, an accurate and efficient streaming data clustering approach is proposed to adapt to the drifting and imbalanced cluster distributions. We first design a self-growth map (SGM) that can automatically arrange neurons on demand according to local distribution, and thus achieve fast and incremental adaptation to the streaming distributions. Since SGM allocates an excess number of density-sensitive neurons to describe the global distribution, it can avoid missing small clusters among imbalanced distributions. We also propose a fast hierarchical merging (HM) strategy to combine the neurons that break up the relatively large clusters. It exploits the maintained SGM to quickly retrieve the intracluster distribution pairs for merging, which circumvents the most laborious global searching. It turns out that the proposed SGM can incrementally adapt to the distributions of new chunks, and the self-growth map-guided hierarchical merging for the imbalanced data clustering (SOHI) approach can quickly explore a true number of imbalanced clusters. Extensive experiments demonstrate that SOHI can efficiently and accurately explore cluster distributions for streaming data.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"148 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144103750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Persistence of Backdoor-Based Watermarks for Neural Networks: A Comprehensive Evaluation 基于后门的神经网络水印持久性的综合评价

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-19 DOI: 10.1109/tnnls.2025.3565170

Anh Tu Ngo, Chuan Song Heng, Nandish Chattopadhyay, Anupam Chattopadhyay

引用次数: 0

Robust Controllability of Boolean Control Networks via Dynamic Programming 基于动态规划的布尔控制网络鲁棒可控性

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-19 DOI: 10.1109/tnnls.2025.3559207

Yakun Li, Shuhua Gao, Yiming Gao, Jianliang Wu, Jun-e Feng, Cheng Xiang

引用次数: 0

Semi-Heterogeneous Graph-Perception Network With Gradient-Weighted Class Activation Mapping for Class-Incremental Industrial Fault Recognition and Root Cause Diagnosis 基于梯度加权类激活映射的半异构图感知网络用于类增量工业故障识别和根本原因诊断

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-19 DOI: 10.1109/tnnls.2025.3567475

Jinping Liu, Sheng Chen, Meiling Cai, Haidong Shao, Weihua Gui

引用次数: 0

Lightweight and Fast Time-Series Anomaly Detection via Point-Level and Sequence-Level Reconstruction Discrepancy 基于点级和序列级重构差异的轻量快速时间序列异常检测

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-19 DOI: 10.1109/tnnls.2025.3565807

Lei Chen, Jiajun Tang, Ying Zou, Xuxin Liu, Xingquan Xie, Guangyang Deng

引用次数: 0

DigNet: Digging Clues From Local–Global Interactive Graph for Aspect-Level Sentiment Classification DigNet：从局部-全局交互图中挖掘线索用于方面级情感分类

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-19 DOI: 10.1109/tnnls.2025.3564306

Bowen Xing, Ivor W. Tsang

引用次数: 0

A Robust Three-Way Classifier With Shadowed Granular Balls Based on Justifiable Granularity 基于合理粒度的阴影颗粒球鲁棒三向分类器

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-19 DOI: 10.1109/tnnls.2025.3563889

Jie Yang, Lingyun Xiaodiao, Guoyin Wang, Witold Pedrycz, Shuyin Xia, Qinghua Zhang, Di Wu

引用次数: 0

A Policy-Guided Reinforcement Learning Method for Encirclement Control in Multiobstacle Environment 多障碍环境下策略导向的强化学习包围圈控制方法

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-16 DOI: 10.1109/tnnls.2025.3566548

Fandi Gou, Haikuo Du, Chenyu Zhao, Yunze Cai

引用次数: 0