arXiv - CS - Machine Learning最新文献_第7页

Modeling Human Responses by Ordinal Archetypal Analysis 通过正序原型分析建立人类反应模型

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.07934

Anna Emilie J. Wedenborg, Michael Alexander Harborg, Andreas Bigom, Oliver Elmgreen, Marcus Presutti, Andreas Råskov, Fumiko Kano Glückstad, Mikkel Schmidt, Morten Mørup

{"title":"Modeling Human Responses by Ordinal Archetypal Analysis","authors":"Anna Emilie J. Wedenborg, Michael Alexander Harborg, Andreas Bigom, Oliver Elmgreen, Marcus Presutti, Andreas Råskov, Fumiko Kano Glückstad, Mikkel Schmidt, Morten Mørup","doi":"arxiv-2409.07934","DOIUrl":"https://doi.org/arxiv-2409.07934","url":null,"abstract":"This paper introduces a novel framework for Archetypal Analysis (AA) tailored\u0000to ordinal data, particularly from questionnaires. Unlike existing methods, the\u0000proposed method, Ordinal Archetypal Analysis (OAA), bypasses the two-step\u0000process of transforming ordinal data into continuous scales and operates\u0000directly on the ordinal data. We extend traditional AA methods to handle the\u0000subjective nature of questionnaire-based data, acknowledging individual\u0000differences in scale perception. We introduce the Response Bias Ordinal\u0000Archetypal Analysis (RBOAA), which learns individualized scales for each\u0000subject during optimization. The effectiveness of these methods is demonstrated\u0000on synthetic data and the European Social Survey dataset, highlighting their\u0000potential to provide deeper insights into human behavior and perception. The\u0000study underscores the importance of considering response bias in cross-national\u0000research and offers a principled approach to analyzing ordinal data through\u0000Archetypal Analysis.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180612","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

Network Anomaly Traffic Detection via Multi-view Feature Fusion 通过多视角特征融合进行网络异常流量检测

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.08020

Song Hao, Wentao Fu, Xuanze Chen, Chengxiang Jin, Jiajun Zhou, Shanqing Yu, Qi Xuan

引用次数: 0

What is the Relationship between Tensor Factorizations and Circuits (and How Can We Exploit it)? 张量因式分解与电路之间的关系是什么（以及如何利用它）？

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.07953

Lorenzo Loconte, Antonio Mari, Gennaro Gala, Robert Peharz, Cassio de Campos, Erik Quaeghebeur, Gennaro Vessio, Antonio Vergari

{"title":"What is the Relationship between Tensor Factorizations and Circuits (and How Can We Exploit it)?","authors":"Lorenzo Loconte, Antonio Mari, Gennaro Gala, Robert Peharz, Cassio de Campos, Erik Quaeghebeur, Gennaro Vessio, Antonio Vergari","doi":"arxiv-2409.07953","DOIUrl":"https://doi.org/arxiv-2409.07953","url":null,"abstract":"This paper establishes a rigorous connection between circuit representations\u0000and tensor factorizations, two seemingly distinct yet fundamentally related\u0000areas. By connecting these fields, we highlight a series of opportunities that\u0000can benefit both communities. Our work generalizes popular tensor\u0000factorizations within the circuit language, and unifies various circuit\u0000learning algorithms under a single, generalized hierarchical factorization\u0000framework. Specifically, we introduce a modular \"Lego block\" approach to build\u0000tensorized circuit architectures. This, in turn, allows us to systematically\u0000construct and explore various circuit and tensor factorization models while\u0000maintaining tractability. This connection not only clarifies similarities and\u0000differences in existing models, but also enables the development of a\u0000comprehensive pipeline for building and optimizing new circuit/tensor\u0000factorization architectures. We show the effectiveness of our framework through\u0000extensive empirical evaluations, and highlight new research opportunities for\u0000tensor factorizations in probabilistic modeling.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180609","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

LoRID: Low-Rank Iterative Diffusion for Adversarial Purification LoRID：逆向纯化的低链迭代扩散

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.08255

Geigh Zollicoffer, Minh Vu, Ben Nebgen, Juan Castorena, Boian Alexandrov, Manish Bhattarai

引用次数: 0

Multiplex Graph Contrastive Learning with Soft Negatives 利用软底片进行多重图对比学习

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.08010

Zhenhao Zhao, Minhong Zhu, Chen Wang, Sijia Wang, Jiqiang Zhang, Li Chen, Weiran Cai

引用次数: 0

GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning GRE^2-MDCL：通过多维对比学习增强图形表示嵌入功能

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.07725

Kaizhe Fan, Quanjun Li

{"title":"GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning","authors":"Kaizhe Fan, Quanjun Li","doi":"arxiv-2409.07725","DOIUrl":"https://doi.org/arxiv-2409.07725","url":null,"abstract":"Graph representation learning has emerged as a powerful tool for preserving\u0000graph topology when mapping nodes to vector representations, enabling various\u0000downstream tasks such as node classification and community detection. However,\u0000most current graph neural network models face the challenge of requiring\u0000extensive labeled data, which limits their practical applicability in\u0000real-world scenarios where labeled data is scarce. To address this challenge,\u0000researchers have explored Graph Contrastive Learning (GCL), which leverages\u0000enhanced graph data and contrastive learning techniques. While promising,\u0000existing GCL methods often struggle with effectively capturing both local and\u0000global graph structures, and balancing the trade-off between nodelevel and\u0000graph-level representations. In this work, we propose Graph Representation\u0000Embedding Enhanced via Multidimensional Contrastive Learning (GRE2-MDCL). Our\u0000model introduces a novel triple network architecture with a multi-head\u0000attention GNN as the core. GRE2-MDCL first globally and locally augments the\u0000input graph using SVD and LAGNN techniques. It then constructs a\u0000multidimensional contrastive loss, incorporating cross-network, cross-view, and\u0000neighbor contrast, to optimize the model. Extensive experiments on benchmark\u0000datasets Cora, Citeseer, and PubMed demonstrate that GRE2-MDCL achieves\u0000state-of-the-art performance, with average accuracies of 82.5%, 72.5%, and\u000081.6% respectively. Visualizations further show tighter intra-cluster\u0000aggregation and clearer inter-cluster boundaries, highlighting the\u0000effectiveness of our framework in improving upon baseline GCL models.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180631","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

Efficient Privacy-Preserving KAN Inference Using Homomorphic Encryption 使用同态加密进行高效的隐私保护 KAN 推断

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.07751

Zhizheng Lai, Yufei Zhou, Peijia Zheng, Lin Chen

{"title":"Efficient Privacy-Preserving KAN Inference Using Homomorphic Encryption","authors":"Zhizheng Lai, Yufei Zhou, Peijia Zheng, Lin Chen","doi":"arxiv-2409.07751","DOIUrl":"https://doi.org/arxiv-2409.07751","url":null,"abstract":"The recently proposed Kolmogorov-Arnold Networks (KANs) offer enhanced\u0000interpretability and greater model expressiveness. However, KANs also present\u0000challenges related to privacy leakage during inference. Homomorphic encryption\u0000(HE) facilitates privacy-preserving inference for deep learning models,\u0000enabling resource-limited users to benefit from deep learning services while\u0000ensuring data security. Yet, the complex structure of KANs, incorporating\u0000nonlinear elements like the SiLU activation function and B-spline functions,\u0000renders existing privacy-preserving inference techniques inadequate. To address\u0000this issue, we propose an accurate and efficient privacy-preserving inference\u0000scheme tailored for KANs. Our approach introduces a task-specific polynomial\u0000approximation for the SiLU activation function, dynamically adjusting the\u0000approximation range to ensure high accuracy on real-world datasets.\u0000Additionally, we develop an efficient method for computing B-spline functions\u0000within the HE domain, leveraging techniques such as repeat packing, lazy\u0000combination, and comparison functions. We evaluate the effectiveness of our\u0000privacy-preserving KAN inference scheme on both symbolic formula evaluation and\u0000image classification. The experimental results show that our model achieves\u0000accuracy comparable to plaintext KANs across various datasets and outperforms\u0000plaintext MLPs. Additionally, on the CIFAR-10 dataset, our inference latency\u0000achieves over 7 times speedup compared to the naive method.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142223714","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

Reimagining Linear Probing: Kolmogorov-Arnold Networks in Transfer Learning 重新想象线性探测：迁移学习中的柯尔莫哥洛夫-阿诺德网络

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.07763

Sheng Shen, Rabih Younes

引用次数: 0

Privacy-preserving federated prediction of pain intensity change based on multi-center survey data 基于多中心调查数据的疼痛强度变化的隐私保护联合预测

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.07997

Supratim Das, Mahdie Rafie, Paula Kammer, Søren T. Skou, Dorte T. Grønne, Ewa M. Roos, André Hajek, Hans-Helmut König, Md Shihab Ullaha, Niklas Probul, Jan Baumbacha, Linda Baumbach

{"title":"Privacy-preserving federated prediction of pain intensity change based on multi-center survey data","authors":"Supratim Das, Mahdie Rafie, Paula Kammer, Søren T. Skou, Dorte T. Grønne, Ewa M. Roos, André Hajek, Hans-Helmut König, Md Shihab Ullaha, Niklas Probul, Jan Baumbacha, Linda Baumbach","doi":"arxiv-2409.07997","DOIUrl":"https://doi.org/arxiv-2409.07997","url":null,"abstract":"Background: Patient-reported survey data are used to train prognostic models\u0000aimed at improving healthcare. However, such data are typically available\u0000multi-centric and, for privacy reasons, cannot easily be centralized in one\u0000data repository. Models trained locally are less accurate, robust, and\u0000generalizable. We present and apply privacy-preserving federated machine\u0000learning techniques for prognostic model building, where local survey data\u0000never leaves the legally safe harbors of the medical centers. Methods: We used\u0000centralized, local, and federated learning techniques on two healthcare\u0000datasets (GLA:D data from the five health regions of Denmark and international\u0000SHARE data of 27 countries) to predict two different health outcomes. We\u0000compared linear regression, random forest regression, and random forest\u0000classification models trained on local data with those trained on the entire\u0000data in a centralized and in a federated fashion. Results: In GLA:D data,\u0000federated linear regression (R2 0.34, RMSE 18.2) and federated random forest\u0000regression (R2 0.34, RMSE 18.3) models outperform their local counterparts\u0000(i.e., R2 0.32, RMSE 18.6, R2 0.30, RMSE 18.8) with statistical significance.\u0000We also found that centralized models (R2 0.34, RMSE 18.2, R2 0.32, RMSE 18.5,\u0000respectively) did not perform significantly better than the federated models.\u0000In SHARE, the federated model (AC 0.78, AUROC: 0.71) and centralized model (AC\u00000.84, AUROC: 0.66) perform significantly better than the local models (AC:\u00000.74, AUROC: 0.69). Conclusion: Federated learning enables the training of\u0000prognostic models from multi-center surveys without compromising privacy and\u0000with only minimal or no compromise regarding model performance.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180610","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

Tera-SpaceCom: GNN-based Deep Reinforcement Learning for Joint Resource Allocation and Task Offloading in TeraHertz Band Space Networks Tera-SpaceCom：基于 GNN 的深度强化学习，用于 TeraHertz 频带空间网络中的联合资源分配和任务卸载

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.07911

Zhifeng Hu, Chong Han, Wolfgang Gerstacker, Ian F. Akyildiz

{"title":"Tera-SpaceCom: GNN-based Deep Reinforcement Learning for Joint Resource Allocation and Task Offloading in TeraHertz Band Space Networks","authors":"Zhifeng Hu, Chong Han, Wolfgang Gerstacker, Ian F. Akyildiz","doi":"arxiv-2409.07911","DOIUrl":"https://doi.org/arxiv-2409.07911","url":null,"abstract":"Terahertz (THz) space communications (Tera-SpaceCom) is envisioned as a\u0000promising technology to enable various space science and communication\u0000applications. Mainly, the realm of Tera-SpaceCom consists of THz sensing for\u0000space exploration, data centers in space providing cloud services for space\u0000exploration tasks, and a low earth orbit (LEO) mega-constellation relaying\u0000these tasks to ground stations (GSs) or data centers via THz links. Moreover,\u0000to reduce the computational burden on data centers as well as resource\u0000consumption and latency in the relaying process, the LEO mega-constellation\u0000provides satellite edge computing (SEC) services to directly compute space\u0000exploration tasks without relaying these tasks to data centers. The LEO\u0000satellites that receive space exploration tasks offload (i.e., distribute)\u0000partial tasks to their neighboring LEO satellites, to further reduce their\u0000computational burden. However, efficient joint communication resource\u0000allocation and computing task offloading for the Tera-SpaceCom SEC network is\u0000an NP-hard mixed-integer nonlinear programming problem (MINLP), due to the\u0000discrete nature of space exploration tasks and sub-arrays as well as the\u0000continuous nature of transmit power. To tackle this challenge, a graph neural\u0000network (GNN)-deep reinforcement learning (DRL)-based joint resource allocation\u0000and task offloading (GRANT) algorithm is proposed with the target of long-term\u0000resource efficiency (RE). Particularly, GNNs learn relationships among\u0000different satellites from their connectivity information. Furthermore,\u0000multi-agent and multi-task mechanisms cooperatively train task offloading and\u0000resource allocation. Compared with benchmark solutions, GRANT not only achieves\u0000the highest RE with relatively low latency, but realizes the fewest trainable\u0000parameters and the shortest running time.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180613","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