IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-09-25 DOI: 10.1109/tpami.2025.3614385

Junwei Cheng,Ke Liang,Pengxing Feng,Weixiong Liu,Yong Tang,Chaobo He

{"title":"Clustering Diffusion Model with Frequency-Signal Modulation for Variational Graph Autoencoders.","authors":"Junwei Cheng,Ke Liang,Pengxing Feng,Weixiong Liu,Yong Tang,Chaobo He","doi":"10.1109/tpami.2025.3614385","DOIUrl":"https://doi.org/10.1109/tpami.2025.3614385","url":null,"abstract":"Variational autoencoders (VAEs) have been widely used for node clustering, with existing methods mainly focusing on enhancing the expressiveness of their latent space. Recently, the integration of diffusion models with VAEs has provided new opportunities to achieve this objective. However, the mechanism by which the diffusion model improves performance remains unclear. To bridge this gap, we conduct an empirical analysis from the perspective of graph spectral theory, revealing that the signal modulation induced by diffusion models closely aligns with the low-frequency spectral characteristics of VAEs, which in turn explains their effectiveness. Nevertheless, further experiments highlight that diffusion models exhibit limitations in modulating high-frequency signals, which diverge from the spectral characteristics of VAEs. Moreover, existing diffusion methods fail to enable the latent space to adequately capture and reflect cluster-specific characteristics. To address these challenges, we propose a novel plug-and-play method, FVD, to improve the performance of VAE-based methods in node clustering tasks. Specifically, we incorporate the graph wavelet transform as a secondary signal modulator, enabling independent adjustments of specific frequency bands to better align with the spectral characteristics of VAEs. Additionally, we introduce the Student's t-distribution as a conditional constraint in the reverse process of FVD, deriving a more compact variational lower bound. This enhancement preserves fine-grained node information while focusing on clustering details, effectively mitigating the cluster collapse phenomenon. Comprehensive experimental results demonstrate that integrating FVD with existing methods achieves competitive performance improvements in most cases.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"1 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145140289","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

Ape Optimizer: A p-Power Adaptive Filter-Based Approach for Deep Learning Optimization. Ape优化器：基于p-Power自适应滤波器的深度学习优化方法。

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-09-25 DOI: 10.1109/tnnls.2025.3610665

Yufei Jin,Han Yang,Xinrui Wang,Yingche Xu,Zhuoran Zhang

{"title":"Ape Optimizer: A p-Power Adaptive Filter-Based Approach for Deep Learning Optimization.","authors":"Yufei Jin,Han Yang,Xinrui Wang,Yingche Xu,Zhuoran Zhang","doi":"10.1109/tnnls.2025.3610665","DOIUrl":"https://doi.org/10.1109/tnnls.2025.3610665","url":null,"abstract":"Deep learning has been widely applied in various domains. Current widely-used optimizers, such as SGD, Adam, and their variants, are designed based on the assumption that the gradient noise generated during model training follows a Gaussian distribution. However, recent empirical studies have found that the gradient noise often does not follow a Gaussian distribution. Instead, the noise exhibits heavy-tailed characteristics consistent with an $alpha $ -stable distribution, casting doubt on the performance and robustness of optimizers designed under the assumption of Gaussian noise. Inspired by the least mean p-power (LMP) algorithm from the field of adaptive filtering, we propose a novel optimizer called Ape for deep learning. Ape integrates a p-power adjustment mechanism to compress large gradients and amplify small ones, mitigating the impact of heavy-tailed gradient distributions. It also employs an approach for estimating second moments tailored to $alpha $ -stable distributions. Extensive experiments on benchmark datasets demonstrate Ape's effectiveness in improving both accuracy and training speed compared to existing optimizers. The Ape optimizer showcases the potential of cross-disciplinary approaches in advancing deep learning optimization techniques and lays the groundwork for future innovations in this domain.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"19 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145140293","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

Generalized open-loop Nash equilibria in linear-quadratic difference games with coupled-affine inequality constraints 具有耦合仿射不等式约束的线性二次差分对策的广义开环纳什均衡

IF 6.8 1区计算机科学

IEEE Transactions on Automatic Control Pub Date : 2025-09-25 DOI: 10.1109/tac.2025.3614441

Partha Sarathi Mohapatra, Puduru Viswanadha Reddy

引用次数: 0

2025 Conference Calendar 2025年会议日历

IF 11.2 2区计算机科学

IEEE Communications Magazine Pub Date : 2025-09-25 DOI: 10.1109/mcom.2025.11180855

引用次数: 0

Generative Digital Twin Channel: Bridging Fully-Decoupled RAN and AI-Native Solutions 生成式数字双通道：桥接完全解耦的RAN和ai原生解决方案

IF 11.2 2区计算机科学

IEEE Communications Magazine Pub Date : 2025-09-25 DOI: 10.1109/mcom.001.2400753

Zongxi Liu, Jiacheng Chen, Yunting Xu, Hongyang Du, Haibo Zhou

引用次数: 0

Series Editorial: Artificial Intelligence and Data Science for Communications 系列社论：通信领域的人工智能和数据科学

IF 11.2 2区计算机科学

IEEE Communications Magazine Pub Date : 2025-09-25 DOI: 10.1109/mcom.2025.11180860

Yongmin Choi, Xingqin Lin, Malamati Louta, Yulin Shao

引用次数: 0

Multi-Agent Transformer-Based Workload Allocation and Worker Selection in Distributed Coded Machine Learning 分布式编码机器学习中基于多智能体转换器的工作负载分配与工作者选择

IF 8.6 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2025-09-25 DOI: 10.1109/tccn.2025.3614383

Yitong Zhou, Qiang Ye, Hui Huang

引用次数: 0

Optimal Energy Management of Microgrid Based on Quantum Annealing Algorithm 基于量子退火算法的微电网最优能量管理

IF 12.3 1区计算机科学

IEEE Transactions on Industrial Informatics Pub Date : 2025-09-25 DOI: 10.1109/tii.2025.3606931

Baonan Wang, Hui Wang, Dan Zhang

引用次数: 0

MOJO: MOtion Pattern Learning and JOint-based Fine-grained Mining for Person Re-identification Based on 4D LiDAR Point Clouds MOJO：运动模式学习和基于关节的细粒度挖掘，基于4D激光雷达点云的人再识别

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-09-25 DOI: 10.1109/tifs.2025.3614500

Zhiyang Lu, Chenglu Wen, Ming Cheng, Cheng Wang

引用次数: 0

Distributed Robustness-and-Safety-Critical Formation Control of Unmanned Aerial Vehicles 无人机分布式鲁棒与安全关键编队控制

IF 4.4 2区计算机科学

IEEE Transactions on Aerospace and Electronic Systems Pub Date : 2025-09-25 DOI: 10.1109/taes.2025.3614205

Kewei Xia, Jiahan Peng, Wei Wang, Yao Zou, Zongyu Zuo

引用次数: 0

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