Neural Networks最新文献_第6页

Decomposition and transfer of individual Q-values for decision-making of multi-agent reinforcement learning with communication 基于沟通的多智能体强化学习决策中个体q值的分解与迁移。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-02-03 DOI: 10.1016/j.neunet.2026.108678

Xiaopeng Xu, Yadong Zhao, Dong Wang

{"title":"Decomposition and transfer of individual Q-values for decision-making of multi-agent reinforcement learning with communication","authors":"Xiaopeng Xu, Yadong Zhao, Dong Wang","doi":"10.1016/j.neunet.2026.108678","DOIUrl":"10.1016/j.neunet.2026.108678","url":null,"abstract":"<div><div>In partially observable multi-agent tasks, communication modules need to be trained to supplement information for decision-making, and decision-making modules need to be trained to analyze received information. This leads to their coupling during the process of multi-agent reinforcement learning. To alleviate the difficulty from the coupling, two transfer-based frameworks are proposed. The Simple Transfer Framework converts a difficult reinforcement learning into an easy one and a supervised learning. The Decomposition and Transfer Framework further decomposes this supervised learning into two simpler ones, based on the decomposition of communication-based individual Q-values. They are decomposed into local individual Q-values and their deviations. Based on this framework, the mutual information neural estimation is introduced to train message generators independently. The proposed frameworks are verified by training two communication-based architectures. They outperform direct reinforcement learning algorithms across various team sizes tested in the Drone Fight. The independent training method effectively facilitates the transfer of the deviations.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108678"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146167535","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

BAED: A new paradigm for few-shot graph learning with explanation in the loop 一个在循环中有解释的少镜头图学习的新范例。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-02-03 DOI: 10.1016/j.neunet.2026.108673

Chao Chen , Xujia Li , Dongsheng Hong , Shanshan Lin , Xiangwen Liao , Chuanyi Liu , Lei Chen

{"title":"BAED: A new paradigm for few-shot graph learning with explanation in the loop","authors":"Chao Chen , Xujia Li , Dongsheng Hong , Shanshan Lin , Xiangwen Liao , Chuanyi Liu , Lei Chen","doi":"10.1016/j.neunet.2026.108673","DOIUrl":"10.1016/j.neunet.2026.108673","url":null,"abstract":"<div><div>The challenges of training and inference in few-shot environments persist in the area of graph representation learning. The quality and quantity of labels are often insufficient due to the extensive expert knowledge required to annotate graph data. In this context, Few-Shot Graph Learning (FSGL) approaches have been developed over the years. Through sophisticated neural architectures and customized training pipelines, these approaches enhance model adaptability to new label distributions. However, compromises in the model’s robustness and interpretability can result in overfitting to noise in labeled data and degraded performance. This paper introduces the first explanation-in-the-loop framework for the FSGL problem, called BAED. We novelly employ the belief propagation algorithm to facilitate label augmentation on graphs. Then, leveraging an auxiliary graph neural network and the gradient backpropagation method, our framework effectively extracts explanatory subgraphs surrounding target nodes. The final predictions are based on these informative subgraphs while mitigating the influence of redundant information from neighboring nodes. Extensive experiments on seven benchmark datasets demonstrate superior prediction accuracy, training efficiency, and explanation quality of BAED. As a pioneer, this work highlights the potential of the explanation-based research paradigm in FSGL.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108673"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158712","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

A punishment neural network-based acceleration-level joint drift-free scheme for solving constrained motion planning problem of redundant robotic manipulators 一种基于惩罚神经网络的冗余机器人关节无漂移约束运动规划方案。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-02-04 DOI: 10.1016/j.neunet.2026.108684

Zhijun Zhang, Xitong Gao, Jinjia Guo

{"title":"A punishment neural network-based acceleration-level joint drift-free scheme for solving constrained motion planning problem of redundant robotic manipulators","authors":"Zhijun Zhang, Xitong Gao, Jinjia Guo","doi":"10.1016/j.neunet.2026.108684","DOIUrl":"10.1016/j.neunet.2026.108684","url":null,"abstract":"<div><div>To solve the repetitive motion problem of redundant robotic manipulators, a punishment neural network-based acceleration-level joint drift-free (PNN-ALJDF) scheme is designed. Traditional joint physical limits constraints are fixed and lack margin. Thus, a novel joint acceleration time-varying constraint is considered in the PNN-ALJDF scheme to avoid the joint state exceeding the physical limits. In addition, to ensure that redundant robotic manipulators can periodically return to the initial pose, a joint drift-free criterion is designed. Furthermore, the joint drift-free criterion, kinematics equation and joint acceleration time-varying constraint are formulated globally as an acceleration-level joint drift-free (ALJDF) scheme by a time-varying quadratic programming approach. Then, the ALJDF scheme is solved by the designed punishment neural network. Thus, the proposed PNN-ALJDF scheme is composed of the ALJDF scheme and punishment neural network. Finally, the simulations demonstrate that the PNN-ALJDF scheme avoids joints from drifting, and the states of joints are all within the acceleration time-varying constraint. In addition, the proposed PNN-ALJDF has higher solution accuracy than the linear variational inequalities-based primal-dual neural network.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108684"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158685","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

C3Net: A cross-modal collaborative calibration of features for object detection using frames and events C3Net：使用帧和事件进行目标检测的跨模态特征协同校准

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-02-02 DOI: 10.1016/j.neunet.2026.108651

Yunhua Chen , Jinyu Zhong , Yihao Guo , Zequan Xie , Jinsheng Xiao , Pinghua Chen

{"title":"C3Net: A cross-modal collaborative calibration of features for object detection using frames and events","authors":"Yunhua Chen , Jinyu Zhong , Yihao Guo , Zequan Xie , Jinsheng Xiao , Pinghua Chen","doi":"10.1016/j.neunet.2026.108651","DOIUrl":"10.1016/j.neunet.2026.108651","url":null,"abstract":"<div><div>Object detection by fusing RGB frames and event streams is challenging due to their inherent heterogeneity and significant statistical disparities, which often lead to suboptimal fusion in existing methods. To address this, we introduce C3Net, a novel framework built upon a paradigm shift from direct feature merging to Collaborative Calibration. First, we propose an Adaptive Balancing Time Surface (ABTS) to generate motion-robust event representations by mitigating spatial inconsistencies caused by varying object velocities. Second, the core Cross-Modal Feature Collaborative Calibration Module (CM-FCCM) performs mutual calibration of RGB and event features across channel and spatial dimensions, reducing modality discrepancies before fusion; the calibrated features are then fed back to the respective backbones for enriched feature learning. Finally, an Adaptive Channel Fusion Module (ACFM) dynamically integrates the modalities based on channel-wise confidence. Extensive experiments on PKU-DAVIS-SOD, DSEC-MOD, and PKU-DDD17-CAR datasets demonstrate that C3Net achieves state-of-the-art performance, showcasing its superior ability to leverage the complementary strengths of frames and events.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108651"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174304","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

RIT-HetGE: A residue interaction type-aware heterogeneous graph-embedding model for predicting protein thermal stability RIT-HetGE：预测蛋白质热稳定性的残基相互作用类型感知异质图嵌入模型

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-02-09 DOI: 10.1016/j.neunet.2026.108707

Lingzhi Liu, Yingying Jiang, Yanbin Gu, Shiming Zhao, Yanrui Ding

{"title":"RIT-HetGE: A residue interaction type-aware heterogeneous graph-embedding model for predicting protein thermal stability","authors":"Lingzhi Liu, Yingying Jiang, Yanbin Gu, Shiming Zhao, Yanrui Ding","doi":"10.1016/j.neunet.2026.108707","DOIUrl":"10.1016/j.neunet.2026.108707","url":null,"abstract":"<div><div>Accurately predicting protein thermal stability is crucial for understanding protein function, guiding protein engineering, and advancing biomedical and industrial applications. Although research on protein thermal stability has advanced with structure-based representation learning, most existing methods embed protein structures as homogeneous graphs, which are unable to capture the intricate and heterogeneous characteristics of residue-residue interactions. To address this limitation, we propose a Residue Interaction Type-Aware Heterogeneous Graph Embedding model (RIT-HetGE) that utilizes intra-interaction-type-aware convolutions for local structure learning and employs an inter-interaction-type-aware attention mechanism to fuse interaction-specific features. Theoretical analysis based on Rademacher complexity provides generalization guarantees. Experiments on a large-scale protein structure dataset demonstrate that our model outperforms baseline models and aggregates diverse interaction types to enhance protein representation. RIT-HetGE also exhibits strong interpretability; the intra-interaction-type-aware layer enables identification of critical residues, whereas the inter-interaction-type-aware layer facilitates the detection of significant interaction types associated with protein thermal stability. These findings highlight the importance of integrating biologically meaningful heterogeneous interactions with protein structure encoding and offer a robust framework for protein thermal stability prediction.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108707"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174305","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

AW-EL-PINNs: A multi-task learning physics-informed neural network for Euler-Lagrange systems in optimal control problems 最优控制问题中Euler-Lagrange系统的多任务学习物理信息神经网络

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-02-09 DOI: 10.1016/j.neunet.2026.108694

Chuandong Li, Runtian Zeng

引用次数: 0

Event-triggered decentralized adaptive critic learning control for interconnected systems with nonlinear inequality state constraints 具有非线性不等式状态约束的互联系统的事件触发分散自适应批评学习控制

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-01-31 DOI: 10.1016/j.neunet.2026.108646

Wenqian Du , Mingduo Lin , Guoling Yuan , Bo Zhao

{"title":"Event-triggered decentralized adaptive critic learning control for interconnected systems with nonlinear inequality state constraints","authors":"Wenqian Du , Mingduo Lin , Guoling Yuan , Bo Zhao","doi":"10.1016/j.neunet.2026.108646","DOIUrl":"10.1016/j.neunet.2026.108646","url":null,"abstract":"<div><div>In this paper, an event-triggered decentralized adaptive critic learning (ACL) control method is proposed for interconnected systems with nonlinear inequality state constraints. First, by introducing a slack function, the nonlinear inequality state constraints of original isolated subsystem are transformed into equality forms, and then the original isolated subsystem is augmented to an unconstrained one. Then, by establishing a cost function with discount factors for each isolated subsystem, a local policy iteration-based decentralized control law is developed by solving the Hamilton–Jacobi–Bellman equation with the help of a local critic neural network (NN) for each isolated subsystem. Through developing a novel event-triggering mechanism for each isolated subsystem, the decentralized control policy is updated at the triggering instants only, which assists to save the computational and communication resources. Hereafter, the event-triggered decentralized control law of isolated subsystem is derived. Then, the overall optimal control for the entire interconnected system is derived by constituting an array of developed event-triggered decentralized control laws. Furthermore, the closed-loop nonlinear interconnected system and the weight estimation errors of local critic NNs are guaranteed to be uniformly ultimately bounded. Finally, the effectiveness of the proposed method is validated through two comparative simulation examples.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108646"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174716","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

HP-GAN: Harnessing pretrained networks for GAN improvement with FakeTwins and discriminator consistency HP-GAN：利用假双胞胎和鉴别器一致性利用预训练网络进行GAN改进。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-01-29 DOI: 10.1016/j.neunet.2026.108666

Geonhui Son , Jeong Ryong Lee , Dosik Hwang

{"title":"HP-GAN: Harnessing pretrained networks for GAN improvement with FakeTwins and discriminator consistency","authors":"Geonhui Son , Jeong Ryong Lee , Dosik Hwang","doi":"10.1016/j.neunet.2026.108666","DOIUrl":"10.1016/j.neunet.2026.108666","url":null,"abstract":"<div><div>Generative Adversarial Networks (GANs) have made significant progress in enhancing the quality of image synthesis. Recent methods frequently leverage pretrained networks to calculate perceptual losses or utilize pretrained feature spaces. In this paper, we extend the capabilities of pretrained networks by incorporating innovative self-supervised learning techniques and enforcing consistency between discriminators during GAN training. Our proposed method, named HP-GAN, effectively exploits neural network priors through two primary strategies: FakeTwins and discriminator consistency. FakeTwins leverages pretrained networks as encoders to compute a self-supervised loss and applies this through the generated images to train the generator, thereby enabling the generation of more diverse and high quality images. Additionally, we introduce a consistency mechanism between discriminators that evaluate feature maps extracted from Convolutional Neural Network (CNN) and Vision Transformer (ViT) feature networks. Discriminator consistency promotes coherent learning among discriminators and enhances training robustness by aligning their assessments of image quality. Our extensive evaluation across seventeen datasets-including scenarios with large, small, and limited data, and covering a variety of image domains-demonstrates that HP-GAN consistently outperforms current state-of-the-art methods in terms of Fréchet Inception Distance (FID), achieving significant improvements in image diversity and quality. Code is available at: <span><span>https://github.com/higun2/HP-GAN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108666"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146114793","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

Enhancing adversarial transferability via curvature-aware penalization 通过曲率感知惩罚增强对抗可转移性。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-01-29 DOI: 10.1016/j.neunet.2026.108665

Cheng Peng , Zeze Tao , Junyu Liu , Jinjia Peng

{"title":"Enhancing adversarial transferability via curvature-aware penalization","authors":"Cheng Peng , Zeze Tao , Junyu Liu , Jinjia Peng","doi":"10.1016/j.neunet.2026.108665","DOIUrl":"10.1016/j.neunet.2026.108665","url":null,"abstract":"<div><div>Transfer-based attack generates adversarial examples on a surrogate model and exploits the intriguing property of transferability to deceive other unknown models, making it practical for real-world scenarios. Recent research has sought to optimize the loss surface by minimizing its maximum loss, which in practice cannot be computed exactly and is instead approximated through gradient ascent. However, the loss landscape becomes increasingly non-linear during later attack stages, making the gradient ascent less effective. To address this challenge, we propose a novel attack called Curvature-Aware Penalization (CAP), which incorporates the gradient norm and the curvature-aware term as regularization terms to maintain the flatness of the loss surface. Since directly computing the Hessian matrix is computationally expensive, we utilize the finite difference method to reduce computational complexity. Specifically, we randomly sample an example from the neighborhood and interpolate gradients at three neighboring points along the example’s gradient direction to approximate the Hessian. Additionally, to reduce the variance caused by random sampling, the combined gradients are averaged over multiple stochastic samples. Comprehensive experimental results demonstrate that our CAP can not only craft adversarial examples with enhanced transferability across various network architectures but also exhibit stronger resistance to state-of-the-art adversarial defense methods. Code is available at <span><span>https://github.com/PC614/CAP</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108665"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146127075","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

DiffMCG: A diffusion model with mask-conditioned guiding module for medical image classification DiffMCG：一种带口罩条件引导模块的医学图像分类扩散模型。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-02-05 DOI: 10.1016/j.neunet.2026.108690

Chen Guan , Haihong Ai , Weiwei Wang , Ravi P. Singh , Shiya Song

{"title":"DiffMCG: A diffusion model with mask-conditioned guiding module for medical image classification","authors":"Chen Guan , Haihong Ai , Weiwei Wang , Ravi P. Singh , Shiya Song","doi":"10.1016/j.neunet.2026.108690","DOIUrl":"10.1016/j.neunet.2026.108690","url":null,"abstract":"<div><div>Diffusion models have application potential in medical image classification tasks due to their effectiveness in eliminating unexpected noise and perturbations from medical images. However, existing diffusion models for medical image classification utilize image features as the condition guiding diffusion model denoising, neglecting the most critical structured semantic information within medical images—namely, the mask of the lesion region. This results in suboptimal denoising performance from diffusion models, consequently impairing classification performance. To address this issue, we propose a diffusion model with the mask-conditioned guiding module called DiffMCG. Specifically, we introduce the Mask-Conditioned Guiding (MCG) module that concurrently extracts features from the medical image and its corresponding mask. Secondly, we design a U-Net denoising network based on the multi-layer perceptron (MLP) that is tailored for low-dimensional vector data and performs denoising tasks within the category label space. Furthermore, we introduce the MMD regularization constraint loss to establish a distributional relationship between the image prediction distribution, mask prediction distribution, and ground-truth label distribution within the label prediction space. This ensures the consistency of multimodal information during the diffusion process. Through analysis of comparative and ablation experiments, we validate the advantages of the MCG module in medical image classification, providing technical support for precision medical diagnostics.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108690"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146167526","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