Neural Networks最新文献_第4页

PCDe: A personalized conversational debiasing framework for next POI recommendation with uncertain check-ins PCDe：一个个性化的会话消除框架，用于不确定签入的下一个POI推荐

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-10 DOI: 10.1016/j.neunet.2025.107443

Chen Li , Guoyan Huang , Zhu Sun , Lu Zhang , Shanshan Feng , Guanfeng Liu

{"title":"PCDe: A personalized conversational debiasing framework for next POI recommendation with uncertain check-ins","authors":"Chen Li , Guoyan Huang , Zhu Sun , Lu Zhang , Shanshan Feng , Guanfeng Liu","doi":"10.1016/j.neunet.2025.107443","DOIUrl":"10.1016/j.neunet.2025.107443","url":null,"abstract":"<div><div>In the next point-of-interest (POI) recommendation, users may visit <em>individual POIs</em> within larger gathering places, such as shopping malls (termed as <em>collective POIs</em>), leading to uncertain check-ins. Our data analysis unveils that (1) the presence of such uncertain check-ins raises a new type of bias, termed as <em>scale bias</em>, that is, the recommender tends to recommend collective POIs over individual POIs, which further exacerbates the commonly-observed <em>popularity bias</em>, that is, the recommender tends to recommend popular POIs rather than unpopular ones; and (2) the existence of the above two types of biases significantly affects the fairness of next POI recommendation with uncertain check-ins. Therefore, we propose a <u>P</u>ersonalized <u>C</u>onversational <u>De</u>biasing framework (PCDe) by exploiting the advantages of conversational techniques to capture personalized dynamic user preferences, thereby mitigating both scale and popularity biases at a personalized level. Specifically, the <em>inquiry component</em> designs an improved question-and-answer manner based on personalized information entropy, thus mitigating the scale bias. The <em>rewarding component</em> then introduces a novel debiasing reward mechanism based on the Jensen–Shannon divergence to make the recommendations better aligned with users’ historical preferences on popularity, thereby addressing the popularity bias. Extensive experiments demonstrate the superiority of our proposed PCDe over state-of-the-arts (SOTAs) regarding mitigating scale and popularity biases while enhancing recommendation accuracy thanks to its personalized debiasing mechanism.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"188 ","pages":"Article 107443"},"PeriodicalIF":6.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844167","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

Solving two-stage stochastic integer programs via representation learning 基于表征学习的两阶段随机整数方案求解

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-10 DOI: 10.1016/j.neunet.2025.107446

Yaoxin Wu , Zhiguang Cao , Wen Song , Yingqian Zhang

引用次数: 0

A multi-domain constraint learning system inspired by adaptive cognitive graphs for emotion recognition 基于自适应认知图的多域约束学习系统

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-09 DOI: 10.1016/j.neunet.2025.107457

Dongrui Gao , Mengwen Liu , Haokai Zhang , Manqing Wang , Hongli Chang , Gaoxiang Ouyang , Shihong Liu , Pengrui Li

{"title":"A multi-domain constraint learning system inspired by adaptive cognitive graphs for emotion recognition","authors":"Dongrui Gao , Mengwen Liu , Haokai Zhang , Manqing Wang , Hongli Chang , Gaoxiang Ouyang , Shihong Liu , Pengrui Li","doi":"10.1016/j.neunet.2025.107457","DOIUrl":"10.1016/j.neunet.2025.107457","url":null,"abstract":"<div><div>Neuroscience shows that the brain stimulated by external information can induce functional responses to emotions, which can be measured and analyzed by electroencephalogram (EEG). Most existing works focus on extracting specific spatial topological information and temporal dependency representations, with a few works begin to mine the value of spatiotemporal cross-domain information. However, these approaches overdependence on cognitive prior information, limiting their ability to grasp complex domain-structured information. Moreover, the stable extraction of cognitive functions is crucial for reinforcing the performance of emotion recognition systems. Here, we propose a multi-domain constraint learning system. It is inspired by adaptive cognitive graphs, embedding spatiotemporal representative knowledge into the constructed framework (AC-DCL) to improve the performance of emotion recognition. In the AC-DCL, a spatial-guided dynamic graph constraint learning module is meticulously designed to overcome reliance on cognitive priors and adaptively generate and constrain functional relationships within cognitive graphs. At the same time, a temporal-driven sequence transformer is proposed to extract global temporal dependency features. Furthermore, this study designs a novel multi-domain interactive attention module with constraining domain-specific differences and aggregating complementary information, which surpasses traditional static cross-domain interactions. The essence of the proposed AC-DCL lies in capturing stable cognitive functions from complex and dynamic cognitive structures. Experimental results on the DREAMER, FACED, and SEED-IV datasets demonstrate the impressive advantages of AC-DCL and its potential to drive the learning of cross-domain interaction representations.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"188 ","pages":"Article 107457"},"PeriodicalIF":6.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817119","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

Compact CNN module balancing between feature diversity and redundancy 兼顾功能多样性和冗余性的紧凑型 CNN 模块

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-09 DOI: 10.1016/j.neunet.2025.107456

Huihuang Zhang, Haigen Hu, Deming Zhou, Xiaoqin Zhang, Bin Cao

引用次数: 0

Dynamic events-based adaptive NN output feedback control of interconnected nonlinear systems under general output constraint 一般输出约束条件下互联非线性系统的基于动态事件的自适应 NN 输出反馈控制

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-09 DOI: 10.1016/j.neunet.2025.107452

Rui Meng , Changchun Hua , Kuo Li , Qidong Li

引用次数: 0

TCH: A novel multi-view dimensionality reduction method based on triple contrastive heads TCH：一种新的基于三重对比头的多视图降维方法

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-09 DOI: 10.1016/j.neunet.2025.107459

Hongjie Zhang , Ruojin Zhou , Siyu Zhao , Ling Jing , Yingyi Chen

{"title":"TCH: A novel multi-view dimensionality reduction method based on triple contrastive heads","authors":"Hongjie Zhang , Ruojin Zhou , Siyu Zhao , Ling Jing , Yingyi Chen","doi":"10.1016/j.neunet.2025.107459","DOIUrl":"10.1016/j.neunet.2025.107459","url":null,"abstract":"<div><div>Multi-view dimensionality reduction (MvDR) is a potent approach for addressing the high-dimensional challenges in multi-view data. Recently, contrastive learning (CL) has gained considerable attention due to its superior performance. However, most CL-based methods focus on promoting consistency between any two cross views from the perspective of subspace samples, which extract features containing redundant information and fail to capture view-specific discriminative information. In this study, we propose feature- and recovery-level contrastive losses to eliminate redundant information and capture view-specific discriminative information, respectively. Based on this, we construct a novel MvDR method based on triple contrastive heads (TCH). This method combines sample-, feature-, and recovery-level contrastive losses to extract sufficient yet minimal subspace discriminative information in accordance with the information bottleneck principle. Furthermore, the relationship between TCH and mutual information is revealed, which provides the theoretical support for the outstanding performance of our method. Our experiments on five real-world datasets show that the proposed method outperforms existing methods.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"188 ","pages":"Article 107459"},"PeriodicalIF":6.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838014","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

Safe and accelerated screening framework for support tensor machines 安全，加速筛选框架支持张量机

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-09 DOI: 10.1016/j.neunet.2025.107458

Xiao Li , Hongmei Wang , Yitian Xu

{"title":"Safe and accelerated screening framework for support tensor machines","authors":"Xiao Li , Hongmei Wang , Yitian Xu","doi":"10.1016/j.neunet.2025.107458","DOIUrl":"10.1016/j.neunet.2025.107458","url":null,"abstract":"<div><div>Support Tensor Machines (STMs) constitute an effective supervised learning method for classifying high-dimensional tensor data. However, traditional iterative solving methods are often time-consuming. To effectively address the issue of lengthy training times, inspired by the safe screening strategies employed in support vector machines, we generalize the safe screening rule to the tensor domain and propose a novel safe screening rule for STM, which includes the dual static screening rule (DSSR), the dynamic screening rule (DGSR), and a subsequent checking verification. The screening rule initially employs variational inequalities to screen out a portion of redundant samples before training, reducing the problem scale. During the training process, the rule further accelerates training by iteratively screening redundant samples using the duality gap. We also design a subsequent checking technique based on optimality conditions to guarantee the safety of the screening rule. Building on this, we also develop a flexible safe screening framework, referred to as DS-DGSR, which incorporates the DSSR and the DGSR. It not only tackles the challenges of combining various tensor decomposition methods and the diverse scenarios of the decomposed coefficient parameter and decomposed samples in STMs, but also offers flexible adaptation and application according to the characteristics of different STMs. Numerical experiments on multiple real-world high-dimensional tensor datasets confirm the effectiveness and feasibility of DS-DGSR.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"188 ","pages":"Article 107458"},"PeriodicalIF":6.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833249","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

Event-triggered control for input-constrained nonzero-sum games through particle swarm optimized neural networks 基于粒子群优化神经网络的输入约束非零和博弈事件触发控制

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-09 DOI: 10.1016/j.neunet.2025.107430

Qiuye Wu , Bo Zhao , Derong Liu

{"title":"Event-triggered control for input-constrained nonzero-sum games through particle swarm optimized neural networks","authors":"Qiuye Wu , Bo Zhao , Derong Liu","doi":"10.1016/j.neunet.2025.107430","DOIUrl":"10.1016/j.neunet.2025.107430","url":null,"abstract":"<div><div>To accommodate the increasing system scale, improve the system operation success rate and save the computational and communication resources, it is urgent to obtain the Nash equilibrium solution for systems with increasing controllers in an effective way. In this paper, nonzero-sum game problem of partially unknown nonlinear systems with input constraints is solved via the particle swarm optimized neural network-based integral reinforcement learning. By introducing the integral reinforcement learning technique, the drift dynamics is not required any more. To further improve the success rate of system operation, extended adaptive particle swarm optimization algorithm which shares the individual historical optimal position with the whole population is adopted in tuning neural network weights, rather than sharing only the current particle in the traditional particle swarm optimization algorithm. The control policy for each player is obtained by solving the coupled Hamilton–Jacobi equation with a single critic neural network, which simplifies the control structure and reduces the computational burden. Moreover, by introducing the event-triggering mechanism, the control policies are updated at event-triggering instants only. Thus, the computational and communication burdens are further reduced. The stability of the closed-loop system is guaranteed by implementing the integral reinforcement learning-based event-triggered control policies via the Lyapunov’s direct method. From the comparative simulation results, the developed integral reinforcement learning-based event-triggered control scheme via the extended adaptive particle swarm optimization performs better than those using gradient descent algorithm, nonlinear programming, particle swarm optimization and other popular training algorithms.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"188 ","pages":"Article 107430"},"PeriodicalIF":6.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807969","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

S4-KD: A single step spiking SiamFC+ + for object tracking with knowledge distillation 基于知识蒸馏的单步峰值siamfc++对象跟踪

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-08 DOI: 10.1016/j.neunet.2025.107478

Wenzhuo Liu , Shuiying Xiang , Tao Zhang , Yanan Han , Yahui Zhang , Xingxing Guo , Licun Yu , Yue Hao

{"title":"S4-KD: A single step spiking SiamFC+ + for object tracking with knowledge distillation","authors":"Wenzhuo Liu , Shuiying Xiang , Tao Zhang , Yanan Han , Yahui Zhang , Xingxing Guo , Licun Yu , Yue Hao","doi":"10.1016/j.neunet.2025.107478","DOIUrl":"10.1016/j.neunet.2025.107478","url":null,"abstract":"<div><div>Spiking neural networks (SNNs), which transmit information through binary spikes, have the advantages of high efficiency and low energy consumption. At present, the multiple time steps of SNNs can lead to increased latency and power consumption. To this end, we propose Single Step Spiking SiamFC+ + (S4), an improved single-step end-to-end direct training target tracking framework that compresses the time step to 1 by temporal pruning, using AlexNet as the backbone network. Experimental results show that, even when only a single time step is used, the tracking performance of the proposed S4 is still comparable to the original Spiking SiamFC+ +. Furthermore, we introduce the knowledge distillation to improve the performance of the proposed S4, which is called S4-KD for clarity. Three kinds of distillation loss functions are designed for the S4-KD. An artificial neural network model based on the AlexNet network serves as the teacher model, while the temporal-pruned S4 model acts as the student model for retraining. Experimental results show that the S4-KD tracker achieves higher performance on several tracking benchmarks. More specifically, on the OTB100 dataset, Precision and Success are 0.871 and 0.657 respectively, on the UAV123 dataset, Precision and Success are 0.766 and 0.603 respectively, and on the VOT2018 dataset, A, R, and EAO are 0.582, 0.370, and 0.278 respectively. In addition, the estimated energy consumption of the S4-KD is only 34.6 % of that of the original Spiking SiamFC+ +. To the best of our knowledge, the proposed S4-KD tracker surpasses all the existing SNN-based object tracking methods, achieving state-of-the-art performance. Our codes will be available at <span><span>https://github.com/PSNN-xd/S4-KD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"188 ","pages":"Article 107478"},"PeriodicalIF":6.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829318","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

PILOT: Deep Siamese network with hybrid attention improves prediction of mutation impact on protein stability PILOT：混合注意力的深度暹罗网络提高了突变对蛋白质稳定性影响的预测

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-08 DOI: 10.1016/j.neunet.2025.107476

Yuan Zhang , Junsheng Deng , Mingyuan Dong , Jiafeng Wu , Qiuye Zhao , Xieping Gao , Dapeng Xiong

{"title":"PILOT: Deep Siamese network with hybrid attention improves prediction of mutation impact on protein stability","authors":"Yuan Zhang , Junsheng Deng , Mingyuan Dong , Jiafeng Wu , Qiuye Zhao , Xieping Gao , Dapeng Xiong","doi":"10.1016/j.neunet.2025.107476","DOIUrl":"10.1016/j.neunet.2025.107476","url":null,"abstract":"<div><div>Evaluating the mutation impact on protein stability (ΔΔ<em>G</em>) is essential in the study of protein engineering and understanding molecular mechanisms of disease-associated mutations. Here, we propose a novel deep learning framework, PILOT, for improved prediction of ΔΔ<em>G</em> using a Siamese network with hybrid attention mechanism. The PILOT framework leverages multiple attention modules to effectively extract representations for amino acids, atoms, and protein sequences, respectively. This approach significantly ensures the deep fusion of structural information at both residue and atom levels, the seamless integration of structural and sequence representations, and the effective capture of both long-range and short-range dependencies among amino acids. Our extensive evaluations demonstrate that PILOT greatly outperforms other state-of-the-art methods. We also showcase that PILOT identifies exceptional patterns for different mutation types. Moreover, we illustrate the clinical applicability of PILOT in highlighting pathogenic variants from benign variants and VUS (variants of uncertain significance), and distinguishing de novo mutations in disease cases and controls. In summary, PILOT presents a robust deep learning tool that could offer significant insights into drug design, medical applications, and protein engineering studies.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"188 ","pages":"Article 107476"},"PeriodicalIF":6.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844169","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