IEEE transactions on artificial intelligence最新文献_第6页

Periodic Hamiltonian Neural Networks 周期哈密尔顿神经网络

IEEE transactions on artificial intelligence Pub Date : 2024-12-30 DOI: 10.1109/TAI.2024.3515934

Zi-Yu Khoo;Dawen Wu;Jonathan Sze Choong Low;Stéphane Bressan

{"title":"Periodic Hamiltonian Neural Networks","authors":"Zi-Yu Khoo;Dawen Wu;Jonathan Sze Choong Low;Stéphane Bressan","doi":"10.1109/TAI.2024.3515934","DOIUrl":"https://doi.org/10.1109/TAI.2024.3515934","url":null,"abstract":"Modeling dynamical systems is a core challenge for science and engineering. Hamiltonian neural networks (HNNs) are state-of-the-art models that regress the vector field of a dynamical system under the learning bias of Hamilton's equations. A recent observation is that embedding biases regarding invariances of the Hamiltonian improve regression performance. One such invariance is the periodicity of the Hamiltonian, which improves extrapolation performance. We propose <italic>periodic HNNs</i> that embed periodicity within HNNs using observational, learning, and inductive biases. An observational bias is embedded by training the HNN on data that reflects the periodicity of the Hamiltonian. A learning bias is embedded through the loss function of the HNN. An inductive bias is embedded by a periodic activation function in the HNN. We evaluate the performance of the proposed models on interpolation and extrapolation problems that either assume knowledge of the periods a priori or learn the periods as parameters. We show that the proposed models can interpolate well but are far more effective than the HNN at extrapolating the Hamiltonian and the vector field for both problems and can even extrapolate the vector field of the chaotic double pendulum Hamiltonian system.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 5","pages":"1194-1202"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revisiting LARS for Large Batch Training Generalization of Neural Networks 神经网络大规模训练泛化的LARS重述

IEEE transactions on artificial intelligence Pub Date : 2024-12-30 DOI: 10.1109/TAI.2024.3523252

Khoi Do;Minh-Duong Nguyen;Nguyen Tien Hoa;Long Tran-Thanh;Nguyen H. Tran;Quoc-Viet Pham

{"title":"Revisiting LARS for Large Batch Training Generalization of Neural Networks","authors":"Khoi Do;Minh-Duong Nguyen;Nguyen Tien Hoa;Long Tran-Thanh;Nguyen H. Tran;Quoc-Viet Pham","doi":"10.1109/TAI.2024.3523252","DOIUrl":"https://doi.org/10.1109/TAI.2024.3523252","url":null,"abstract":"This article investigates large batch training techniques using layer-wise adaptive scaling ratio (LARS) across diverse settings. In particular, we first show that a state-of-the-art technique, called LARS with the warm-up, tends to be trapped in sharp minimizers early on due to redundant ratio scaling. Additionally, a fixed steep decline in the latter phase restricts deep neural networks from effectively navigating early-phase sharp minimizers. To address these issues, we propose time varying LARS (TVLARS), a novel algorithm that replaces warm-up with a configurable sigmoid-like function for robust training in the initial phase. TVLARS promotes gradient exploration early on, surpassing sharp optimizers and gradually transitioning to LARS for robustness in later stages. Extensive experiments demonstrate that TVLARS consistently outperforms LARS and LAMB in most cases, with up to 2% improvement in classification scenarios. In all self-supervised learning cases, TVLARS achieves up to 10% performance improvement. Our implementation is available at <uri>https://github.com/KhoiDOO/tvlars</uri>.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 5","pages":"1321-1333"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817779","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multilabel Black-Box Adversarial Attacks Only With Predicted Labels 仅使用预测标签的多标签黑盒对抗性攻击

IEEE transactions on artificial intelligence Pub Date : 2024-12-27 DOI: 10.1109/TAI.2024.3522869

Linghao Kong;Wenjian Luo;Zipeng Ye;Qi Zhou;Yan Jia

{"title":"Multilabel Black-Box Adversarial Attacks Only With Predicted Labels","authors":"Linghao Kong;Wenjian Luo;Zipeng Ye;Qi Zhou;Yan Jia","doi":"10.1109/TAI.2024.3522869","DOIUrl":"https://doi.org/10.1109/TAI.2024.3522869","url":null,"abstract":"Multilabel adversarial examples have become a threat to deep neural network models (DNNs). Most of the current work on multilabel adversarial examples are focused on white-box environments. In this article, we focus on a black-box environment where the available information is extremely limited: a label-only black-box environment. Under the label-only black-box environment, the attacker can only obtain the predicted labels, and cannot obtain any other information such as the model's internal structure, parameters, the training dataset, and the output prediction confidence. We propose a label-only black-box attack framework, and through this framework to implement two black-box adversarial attacks: multi-label boundary-based attack (ML-BA) and multilabel label-only black-box attack (ML-LBA). The ML-BA is developed by transplanting the boundary-based attack in the multiclass domain to the multilabel domain, and the ML-LBA is based on differential evolution. Experimental results show that both the proposed algorithms can achieve the hiding single label attack in label-only black-box environments. Besides, ML-LBA requires fewer queries and its perturbations are significantly less. This demonstrates the effectiveness of the proposed label-only black-box attack framework and the advantageous of differential evolution in optimizing high-dimensional problems.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 5","pages":"1284-1297"},"PeriodicalIF":0.0,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VODACBD: Vehicle Object Detection Based on Adaptive Convolution and Bifurcation Decoupling 基于自适应卷积和分岔解耦的车辆目标检测

IEEE transactions on artificial intelligence Pub Date : 2024-12-27 DOI: 10.1109/TAI.2024.3522871

Yunfei Yin;Zheng Yuan;Yu He;Xianjian Bao

{"title":"VODACBD: Vehicle Object Detection Based on Adaptive Convolution and Bifurcation Decoupling","authors":"Yunfei Yin;Zheng Yuan;Yu He;Xianjian Bao","doi":"10.1109/TAI.2024.3522871","DOIUrl":"https://doi.org/10.1109/TAI.2024.3522871","url":null,"abstract":"Vehicle object detection is the foundation of autonomous driving system development. The existing state-of-the-art methods mainly focus on the applications and improvement of general-purpose single shot multibox detector (SSD) and you only look once (YOLO) methods. However, these methods overlook the specific characteristics of traffic scenarios, such as frequent changes of camera angles and rapid changes in surrounding environment, thus leading to peculiar deformations and blurring of vehicle objects. To address these issues, we consider making improvements on the targeted deformations, classification, positioning, and other operations for vehicle object images to alleviate the object deformation and blurring, and therefore propose a Vehicle Object Detection method based on adaptive convolution and bifurcation decoupling (VODACBD). Specifically, in VODACBD, to solve the deformation problem of vehicle objects, adaptive convolution, and feature redivision upsampling are proposed to dynamically capture object features; to alleviate the blurring of vehicle objects, a bifurcation decoupling head is proposed to learn vehicle categories, positions, and confidences. Moreover, to further enhance the overall performance, a global optimal transportation algorithm (GlobalOTA) is well designed to improve the quality of training samples. Extensive experiments were conducted on publicly available traffic object detection datasets such as BDD100K, KITTI, and VOC. The experimental results demonstrate that, compared with current state-of-the-art methods, VODACBD not only achieves an average performance improvement of 1.4% but also an average speed improvement of 1.57 times that of the state-of-the-art.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 5","pages":"1298-1308"},"PeriodicalIF":0.0,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the Role of Priors in Bayesian Causal Learning 论先验在贝叶斯因果学习中的作用

IEEE transactions on artificial intelligence Pub Date : 2024-12-25 DOI: 10.1109/TAI.2024.3522867

Bernhard C. Geiger;Roman Kern

引用次数: 0

Learning to Communicate Among Agents for Large-Scale Dynamic Path Planning With Genetic Programming Hyperheuristic 基于遗传规划超启发式的大规模动态路径规划智能体间通信学习

IEEE transactions on artificial intelligence Pub Date : 2024-12-25 DOI: 10.1109/TAI.2024.3522861

Xiao-Cheng Liao;Xiao-Min Hu;Xiang-Ling Chen;Yi Mei;Ya-Hui Jia;Wei-Neng Chen

{"title":"Learning to Communicate Among Agents for Large-Scale Dynamic Path Planning With Genetic Programming Hyperheuristic","authors":"Xiao-Cheng Liao;Xiao-Min Hu;Xiang-Ling Chen;Yi Mei;Ya-Hui Jia;Wei-Neng Chen","doi":"10.1109/TAI.2024.3522861","DOIUrl":"https://doi.org/10.1109/TAI.2024.3522861","url":null,"abstract":"Genetic programming hyperheuristic (GPHH) has recently become a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lack a global systemic view sometimes may be problematic. Therefore, a new challenge is to strike a balance between conciseness to improve generalization ability and incorporation of more global information to obtain better performance. In this work, we target the LDPP problem and propose a communication learning mechanism (ComLGP) for GPHH to address the above difficulties. In ComLGP, a communication function is introduced to serve as a communication protocol and exist in the form of an extra terminal in GPHH. Compared to the classic terminals which are fixed in genetic programing, this communication function undergoes optimization along with the evolutionary process of GPHH. In this way, the communication function can be learned which enables agents to communicate without a predefined communication protocol. Then, a caching and lazy updating mechanism for ComLGP is presented to accelerate the calculation of communication content. Last, we verified our method on 22 scenarios including two real world road networks. The experimental results demonstrate that the proposed ComLGP can successfully learn to communicate. Although in the absence of any manually designed communication features, ComLGP is capable of achieving performance competitive to the state-of-the-art method that employs a predefined communication protocol and outperforms the remaining compared methods in most scenarios.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 5","pages":"1269-1283"},"PeriodicalIF":0.0,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Seeking Secure Synchronous Tracking of Networked Agent Systems Subject to Antagonistic Interactions and Denial-of-Service Attacks 对抗交互和拒绝服务攻击下网络代理系统的安全同步跟踪研究

IEEE transactions on artificial intelligence Pub Date : 2024-12-25 DOI: 10.1109/TAI.2024.3522873

Weihao Li;Lei Shi;Mengji Shi;Jiangfeng Yue;Boxian Lin;Kaiyu Qin

{"title":"Seeking Secure Synchronous Tracking of Networked Agent Systems Subject to Antagonistic Interactions and Denial-of-Service Attacks","authors":"Weihao Li;Lei Shi;Mengji Shi;Jiangfeng Yue;Boxian Lin;Kaiyu Qin","doi":"10.1109/TAI.2024.3522873","DOIUrl":"https://doi.org/10.1109/TAI.2024.3522873","url":null,"abstract":"Inspired by the group phenomenon of biological populations in nature, swarm intelligence has been derived and has further advanced the research of coordinated control of networked agent systems (NASs). With this in mind, this article delves into the problem of secure synchronous tracking control for high-order NASs subject to antagonistic interactions, particularly under the threat of denial-of-service (DoS) attacks. First, a novel distributed secure control scheme is crafted to address the complex dynamics of NASs that encompass both cooperative and antagonistic interactions among agents. This scheme is pivotal as it enables follower agents to synchronize their tracking with the leader agent, even amidst the disruptive influence of DoS attacks, transcending the conventional bipartite tracking consensus approach. Subsequently, a dynamic, time-varying closed-loop system is generated, which is intrinsically linked to the intermittent nature of DoS attacks, characterized by periods of dormancy and activity. Based on the infinite matrix product convergence analysis method, some essential algebraic conditions are formulated, which hinge on the parameters of DoS attacks, the underlying network structure, and the gain of the controller. These conditions are critical for guaranteeing the attainment of robust synchronous tracking. Finally, some numerical simulation examples are provided to verify the effectiveness of the proposed secure synchronous tracking control scheme for high-order NASs with signed networks. That is, all followers are able to achieve synchronous tracking of the leader when the corresponding topology, as well as control parameter conditions, are satisfied, and the opposite is not possible.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 5","pages":"1309-1320"},"PeriodicalIF":0.0,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Temporally Recursive Differencing Network for Anomaly Detection in Videos 视频异常检测的深度时间递归差分网络

IEEE transactions on artificial intelligence Pub Date : 2024-12-23 DOI: 10.1109/TAI.2024.3521877

Gargi V. Pillai;Debashis Sen

{"title":"Deep Temporally Recursive Differencing Network for Anomaly Detection in Videos","authors":"Gargi V. Pillai;Debashis Sen","doi":"10.1109/TAI.2024.3521877","DOIUrl":"https://doi.org/10.1109/TAI.2024.3521877","url":null,"abstract":"Intelligent video surveillance systems with anomaly detection capabilities are indispensable for outdoor security. Video anomaly detection (VAD) is usually performed by learning patterns representing normal events and declaring an anomaly when an abnormal pattern is encountered. However, the features of normal patterns in a video often vary with time as real-world videos are non-stationary in nature, which makes its handling essential during VAD. To this end, we propose an approach for anomaly detection in videos, where a novel deep temporally recursive differencing network (DDN) diminishes the adverse effects of the non-stationary nature on VAD. The DDN consists of multiple layers of differencing operators of optimized orders, where every two consecutive layers are separated by a suitable nonlinearity. Spatial and temporal features are extracted from nonoverlapping blocks in video frames and fed to the DDN. While the spatial feature is obtained using a pretrained network, our temporal feature computation involves the use of FlowNetS with a new training strategy that does not require ground truth. The features at the output of DDN are used in a predictor based on autoregression and moving average of the regression errors. Then, the predictor's output estimates are compared to the corresponding actual values for anomaly detection, which also involves block-level selection and consistency check. Qualitative evaluation and quantitative comparison with several existing approaches on multiple standard datasets demonstrate the effectiveness of the proposed VAD approach. An ablation study highlighting the significance of the various components of our approach and a hyperparameter analysis are also provided.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 5","pages":"1414-1428"},"PeriodicalIF":0.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Intelligent Chatbot Assistant for Comprehensive Troubleshooting Guidelines and Knowledge Repository in Printed Circuit Board Production 用于印刷电路板生产中综合故障排除指南和知识库的智能聊天机器人助手

IEEE transactions on artificial intelligence Pub Date : 2024-12-23 DOI: 10.1109/TAI.2024.3521873

Supparesk Rittikulsittichai;Thitirat Siriborvornratanakul

引用次数: 0

Improved Supervised Machine Learning for Predicting Auto Insurance Purchase Patterns 预测汽车保险购买模式的改进监督机器学习

IEEE transactions on artificial intelligence Pub Date : 2024-12-23 DOI: 10.1109/TAI.2024.3521870

Mourad Nachaoui;Fatma Manlaikhaf;Soufiane Lyaqini

引用次数: 0