Neurocomputing最新文献_第9页

Adaptive finite-time tracking control for stochastic nonlinear systems based on IT2FNN 基于IT2FNN的随机非线性系统自适应有限时间跟踪控制

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-04-28 Epub Date: 2026-02-09 DOI: 10.1016/j.neucom.2026.133013

Shuangyun Xing , Mingchen Wei , Feiqi Deng , Xueyan Zhao , Fengjun Xiao

引用次数: 0

Odin: Oriented dual-module integration for text-rich network representation learning 面向双模块集成的富文本网络表示学习

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-04-28 Epub Date: 2026-02-09 DOI: 10.1016/j.neucom.2026.133018

Kaifeng Hong, Yinglong Zhang, Xiaoying Hong, Xuewen Xia, Xing Xu

{"title":"Odin: Oriented dual-module integration for text-rich network representation learning","authors":"Kaifeng Hong, Yinglong Zhang, Xiaoying Hong, Xuewen Xia, Xing Xu","doi":"10.1016/j.neucom.2026.133018","DOIUrl":"10.1016/j.neucom.2026.133018","url":null,"abstract":"<div><div>Text-attributed graphs require models to effectively combine strong textual understanding with structurally informed reasoning. Existing approaches either rely on GNNs—limited by over-smoothing and hop-dependent diffusion—or employ Transformers that largely overlook graph topology and treat nodes as isolated sequences. We propose <strong>Odin</strong> (<strong>O</strong>riented <strong>d</strong>ual-module <strong>in</strong>tegration), a new architecture that injects graph structure into Transformers at selected depths through an oriented dual-module mechanism. Unlike message-passing GNNs, Odin does not rely on multi-hop diffusion; instead, multi-hop structures are integrated at specific Transformer layers, yielding low-, mid-, and high-level structural abstraction aligned with the model’s semantic hierarchy. Because aggregation operates on node-specific [CLS] representations induced by textual tokens, Odin mitigates over-smoothing by preventing the iterative diffusion of homogeneous hidden states, and decouples structural abstraction from neighborhood size or graph topology. We further establish that Odin’s expressive power strictly contains that of both pure Transformers and GNNs. To make the design efficient in large-scale or low-resource settings, we introduce Light Odin, a lightweight variant that preserves the same layer-aligned structural abstraction for faster training and inference. Experiments on multiple text-rich graph benchmarks show that Odin achieves state-of-the-art accuracy, while Light Odin delivers competitive performance with significantly reduced computational cost. Together, Odin and Light Odin form a unified, hop-free framework for principled structure–text integration. The source code for this model has been released at <span><span>https://github.com/hongkaifeng/Odin</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"675 ","pages":"Article 133018"},"PeriodicalIF":6.5,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

TabNSA: Native sparse attention for efficient tabular data learning TabNSA：用于高效表格数据学习的原生稀疏关注

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-04-28 Epub Date: 2026-02-05 DOI: 10.1016/j.neucom.2026.132928

Ali Eslamian , Qiang Cheng

{"title":"TabNSA: Native sparse attention for efficient tabular data learning","authors":"Ali Eslamian , Qiang Cheng","doi":"10.1016/j.neucom.2026.132928","DOIUrl":"10.1016/j.neucom.2026.132928","url":null,"abstract":"<div><div>Tabular data poses unique challenges for deep learning due to its heterogeneous feature types, lack of spatial structure, and often limited sample sizes. We propose TabNSA, a novel deep learning framework that integrates Native Sparse Attention (NSA) with a TabMixer backbone to efficiently model tabular data. TabNSA tackles computational and representational challenges by dynamically focusing on relevant feature subsets per instance. The NSA module employs a hierarchical sparse attention mechanism, including token compression, selective preservation, and localized sliding windows, to significantly reduce the quadratic complexity of standard attention operations while addressing feature heterogeneity. Complementing this, the TabMixer backbone captures complex, non-linear dependencies through parallel multilayer perceptron (MLP) branches with independent parameters. These modules are synergistically combined via element-wise summation and mean pooling, enabling TabNSA to model both global context and fine-grained interactions. Extensive experiments across supervised and transfer learning settings show that TabNSA consistently outperforms state-of-the-art deep learning models. Furthermore, by augmenting TabNSA with a fine-tuned large language model (LLM), we enable it to effectively address Few-Shot Learning challenges through language-guided generalization on diverse tabular benchmarks. <strong>Code available on:</strong> <span><span>https://github.com/aseslamian/TabNSA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"675 ","pages":"Article 132928"},"PeriodicalIF":6.5,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Robust domain adaptation using gram optimal transport for high variance environments 基于gram最优传输的高方差环境鲁棒域自适应

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-04-28 Epub Date: 2026-02-10 DOI: 10.1016/j.neucom.2026.132965

Khushboo Mishra , Tanima Dutta

{"title":"Robust domain adaptation using gram optimal transport for high variance environments","authors":"Khushboo Mishra , Tanima Dutta","doi":"10.1016/j.neucom.2026.132965","DOIUrl":"10.1016/j.neucom.2026.132965","url":null,"abstract":"<div><div>High-variance environments characterized by substantial distribution shifts, label noise, and complex internal data relationships pose a fundamental challenge for reliable machine learning. In such settings, domain adaptation (DA) becomes especially difficult, as conventional methods often assume moderate divergence between source and target domains and overlook structural noise and data heterogeneity. These assumptions lead to performance degradation and instability when applied to high-variance scenarios. To address this, we propose a robust DA framework that leverages the expressive power of Gram matrices and the flexibility of Optimal Transport (OT) to align distributions while preserving intra-domain structure. Unlike traditional OT methods, which rely primarily on pairwise geometric distances and often ignore higher-order feature dependencies, our approach embeds Gram-based similarity networks to model relational patterns within and across domains. This enables the capture of semantic consistency beyond mere pointwise alignment. A key innovation of our method is the introduction of a low-rank constraint on the Gram matrix, which acts as a structural regularizer to suppress noise and highlight dominant data subspaces. Traditional OT formulations are susceptible to corruption in high-variance settings, as they treat all feature dimensions equally and can overfit to noisy or outlier-dominated regions. In contrast, our rank-constrained transport plan selectively emphasizes coherent, low-dimensional structures within the source domain, effectively filtering out corrupted subspaces and enhancing the robustness of cross-domain alignment. Experimental results across multiple benchmarks demonstrate that our approach significantly improves adaptation robustness, achieving an average accuracy of 89.36% on Office-Caltech, with gains of +3.85% on Office-Home, +2.5% on DomainNet, and +5.08% on the imbalanced VisDA dataset.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"675 ","pages":"Article 132965"},"PeriodicalIF":6.5,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 linear encryption privacy protection strategy against eavesdroppers in cyber-physical systems 网络物理系统中针对窃听者的线性加密隐私保护策略

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-04-28 Epub Date: 2026-02-10 DOI: 10.1016/j.neucom.2026.132937

Shaojie Xu , Dan Ye , Dongsheng Yang , Guangdi Li

引用次数: 0

CaPGNN: Optimizing parallel graph neural network training with joint caching and resource-aware graph partitioning CaPGNN：利用联合缓存和资源感知图分区优化并行图神经网络训练

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-04-28 Epub Date: 2026-02-07 DOI: 10.1016/j.neucom.2026.132978

Xianfeng Song, Yi Zou, Zheng Shi

{"title":"CaPGNN: Optimizing parallel graph neural network training with joint caching and resource-aware graph partitioning","authors":"Xianfeng Song, Yi Zou, Zheng Shi","doi":"10.1016/j.neucom.2026.132978","DOIUrl":"10.1016/j.neucom.2026.132978","url":null,"abstract":"<div><div>Graph-structured data is ubiquitous in the real world, and Graph Neural Networks (GNNs) have become increasingly popular in various fields due to their ability to process such irregular data directly. However, as data scales, GNNs become inefficient. Although parallel training offers performance improvements, increased communication costs often offset these advantages. To address this, this paper introduces CaPGNN, a novel parallel full-batch GNN training framework on single server with multi-GPU. Firstly, considering the fact that the number of remote vertices in a partition is often greater than or equal to the number of local vertices and there may exist many duplicate vertices, we propose a joint adaptive caching algorithm that leverages both CPU and GPU memory, integrating lightweight cache update and prefetch techniques to effectively reduce redundant communication costs. Furthermore, taking into account the varying computational and communication capabilities among GPUs, we propose a communication- and computation-aware heuristic graph partitioning algorithm inspired by graph sparsification. Additionally, we implement a pipeline to overlap computation and communication. Extensive experiments show that CaPGNN improves training efficiency by up to 18.98x and reduces communication costs by up to 99%, with minimal accuracy loss or even accuracy improvement in some cases. Finally, we extend CaPGNN to multi-machine multi-GPU environments. The code is available at <span><span>https://github.com/songxf1024/CaPGNN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"675 ","pages":"Article 132978"},"PeriodicalIF":6.5,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

T-SFANet: A spatial-frequency attention network for thermal infrared image dehazing T-SFANet：一种用于热红外图像去雾的空频关注网络

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-04-28 Epub Date: 2026-01-24 DOI: 10.1016/j.neucom.2026.132797

Jiayi Li , Minhua Zheng , Chaoyang Sun , Zengshuai Wang , Haikuo Shen

{"title":"T-SFANet: A spatial-frequency attention network for thermal infrared image dehazing","authors":"Jiayi Li , Minhua Zheng , Chaoyang Sun , Zengshuai Wang , Haikuo Shen","doi":"10.1016/j.neucom.2026.132797","DOIUrl":"10.1016/j.neucom.2026.132797","url":null,"abstract":"<div><div>Thermal infrared (TIR) image dehazing aims to mitigate degradation caused by haze-induced scattering and absorption, thereby restoring structural clarity and detail visibility. Previous dehazing approaches have been developed for visible images, and their direct application to TIR data is limited due to fundamental differences in radiation characteristics and texture distribution. To this end, T-SFANet is proposed as a spatial-frequency attention network specifically designed for TIR image dehazing. A Local-Global Alternating Attention block is designed, which alternates between local window attention and sparse global attention to simultaneously preserve fine textures and capture large-scale haze structures. In addition, a Frequency-domain Self-Attention mechanism and a Band FFT Loss are incorporated to enhance the recovery of mid- and high-frequency components, improving detail and edge reconstruction. Furthermore, a real-world dataset named TIR-HAZE is constructed, representing the first TIR dehazing benchmark collected under natural haze conditions. Experiments on both M3FD-Synthetic and TIR-HAZE demonstrate that T-SFANet achieves superior restoration accuracy compared to state-of-the-art methods in terms of visual quality and quantitative evaluation.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"675 ","pages":"Article 132797"},"PeriodicalIF":6.5,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

WAVE++: Capturing within-task variance for continual relation extraction with adaptive prompting 捕获任务内方差，通过自适应提示持续提取关系

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-04-28 Epub Date: 2026-02-05 DOI: 10.1016/j.neucom.2026.132915

Bao-Ngoc Dao , Minh Le , Quang Nguyen , Luyen Ngo Dinh , Nam Le Hai, Linh Ngo Van

{"title":"WAVE++: Capturing within-task variance for continual relation extraction with adaptive prompting","authors":"Bao-Ngoc Dao , Minh Le , Quang Nguyen , Luyen Ngo Dinh , Nam Le Hai, Linh Ngo Van","doi":"10.1016/j.neucom.2026.132915","DOIUrl":"10.1016/j.neucom.2026.132915","url":null,"abstract":"<div><div>Memory-based approaches have shown strong performance in Continual Relation Extraction (CRE). However, storing examples from previous tasks increases memory usage and raises privacy concerns. Recently, prompt-based methods have emerged as a promising alternative, as they do not rely on storing past samples. Despite this progress, current prompt-based techniques face several core challenges in CRE, particularly in accurately identifying task identities and mitigating catastrophic forgetting. Existing prompt selection strategies often suffer from inaccuracies, lack robust mechanisms to prevent forgetting in shared parameters, and struggle to handle both cross-task and within-task variations. In this paper, we propose <strong>WAVE++</strong>, a novel approach inspired by the connection between prefix-tuning and mixture of experts. Specifically, we introduce task-specific prompt pools that enhance flexibility and adaptability across diverse tasks while avoiding boundary-spanning risks; this design more effectively captures both within-task and cross-task variations. To further refine relation classification, we incorporate label descriptions that provide richer, more global context, enabling the model to better distinguish among different relations. We also propose a training-free mechanism to improve task prediction during inference. Moreover, we integrate a generative model to consolidate prior knowledge within the shared parameters, thereby removing the need for explicit data storage. Extensive experiments demonstrate that WAVE++ outperforms state-of-the-art prompt-based and rehearsal-based methods, offering a more robust solution for continual relation extraction. Our code is publicly available at <span><span>https://github.com/PiDinosauR2804/WAVE-CRE-PLUS-PLUS</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"675 ","pages":"Article 132915"},"PeriodicalIF":6.5,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 shape bias for object detection 增强物体检测的形状偏差

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-04-28 Epub Date: 2026-02-06 DOI: 10.1016/j.neucom.2026.132931

Jiwen Tang , Gu Wang , Ruida Zhang , Xiangyang Ji

{"title":"Enhancing shape bias for object detection","authors":"Jiwen Tang , Gu Wang , Ruida Zhang , Xiangyang Ji","doi":"10.1016/j.neucom.2026.132931","DOIUrl":"10.1016/j.neucom.2026.132931","url":null,"abstract":"<div><div>Convolutional Neural Networks (CNNs) are widely used for object detection tasks, whereas recent studies have shown that they rely more on texture rather than shape for object recognition, a phenomenon known as texture bias. This bias makes them vulnerable to image corruptions, domain shifts, and adversarial perturbations, posing significant challenges for real-world deployment, especially in safety-critical and industrial applications. Despite its significance, texture bias in object detection remains largely underexplored. To address this gap, we first conduct a comprehensive analysis of texture bias across multiple widely-used CNN-based detection architectures, demonstrating the widespread presence and detrimental impact of this issue. Motivated by these findings, we propose a simple yet effective method, TexDrop, to increase shape bias in CNNs and therefore improve their accuracy and robustness. Specifically, TexDrop randomly drops out the texture and color of the training images through straightforward edge detection, forcing models to learn to detect objects based on their shape, thus increasing shape bias. Unlike prior approaches that require architectural modifications, extensive additional training data or complex regularization schemes, TexDrop is model-agnostic, easy to integrate into existing training pipelines, and incurs negligible computational overhead. Intensive experiments on Pascal VOC, COCO, and various corrupted COCO datasets demonstrate that TexDrop not only improves detection performance across multiple architectures but also consistently enhances robustness against various image corruptions and texture variations. Our study provides empirical insights into texture dependence in object detectors and contributes a practical solution for developing more robust and reliable object detection systems in real-world applications.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"675 ","pages":"Article 132931"},"PeriodicalIF":6.5,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Weightless multi-class classifier ruled by “Mental images” 由“心理意象”支配的失重多类分类器

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-04-28 Epub Date: 2026-02-04 DOI: 10.1016/j.neucom.2026.132975

Antonio Sorgente, Massimo De Gregorio

{"title":"Weightless multi-class classifier ruled by “Mental images”","authors":"Antonio Sorgente, Massimo De Gregorio","doi":"10.1016/j.neucom.2026.132975","DOIUrl":"10.1016/j.neucom.2026.132975","url":null,"abstract":"<div><div>Common-sense rules represent a form of implicit knowledge derived from experience and observation, often used by both humans and artificial systems to guide decision-making. In this work, we introduce a novel extension of the DRASiW weightless neural network architecture that enables the automatic extraction of such rules from its internal knowledge representations, known as “Mental Images” (MIs). These rules, grounded in statistical patterns learned during training, are used to selectively inhibit a subset of discriminators prior to classification, effectively acting as a pre-filtering mechanism. The inhibition process is designed to reduce the computational burden and improve classification plausibility by narrowing the focus to the most promising classes. Unlike previous approaches that use MIs solely for interpretation or symbolic conversion, our method exploits this implicit knowledge to enhance the system’s own performance. The extracted rules reflect structural regularities within the input space and serve as a proxy for common sense reasoning embedded within the trained model.</div><div>This work highlights how internal representations in weightless neural systems can be explicitly leveraged to support interpretable and efficient decision processes, bridging the gap between subsymbolic learning and symbolic reasoning through rule-based inhibition.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"675 ","pages":"Article 132975"},"PeriodicalIF":6.5,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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