{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/anomaly-detection/papers/21","list_of":"/task/anomaly-detection","task":"Anomaly Detection","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":21,"pages_in_order":49,"rows_per_page":100,"rows":[2001,2100],"of":4856,"counts":{"archive_papers_tagged":4856,"with_a_code_link":1727,"where_syntology_ran_a_sample":347,"not_listed_spam_title":0,"listed":4856,"listed_where_code_ran":347,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":294,"every_run_a_failure_of_syntologys_instrument":53,"listed_with_a_run_with_no_instrument_failure":294,"listed_every_run_a_failure_of_syntologys_instrument":53,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/anomaly-detection","prev":"/task/anomaly-detection/papers/20","next":"/task/anomaly-detection/papers/22","papers":[{"url":null,"slug":"cybersentinel-an-emergent-threat-detection","title":"CyberSentinel: An Emergent Threat Detection System for AI Security","date":"2025-02-20","arxiv_id":"2502.14966","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-anomaly-detection-via-adaptive-test","title":"Graph Anomaly Detection via Adaptive Test-time Representation Learning across Out-of-Distribution Domains","date":"2025-02-20","arxiv_id":"2502.14293","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-synergy-scoring-filter-for-unsupervised","title":"A Synergy Scoring Filter for Unsupervised Anomaly Detection with Noisy Data","date":"2025-02-19","arxiv_id":"2502.13992","repositories_listed":0,"syntology":null},{"url":null,"slug":"flow-based-generative-models-as-iterative","title":"Flow-based generative models as iterative algorithms in probability space","date":"2025-02-19","arxiv_id":"2502.13394","repositories_listed":0,"syntology":null},{"url":null,"slug":"unlocking-multimodal-integration-in-ehrs-a","title":"Unlocking Multimodal Integration in EHRs: A Prompt Learning Framework for Language and Time Series Fusion","date":"2025-02-19","arxiv_id":"2502.13509","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-anomaly-detection-in-cyber","title":"A Survey of Anomaly Detection in Cyber-Physical Systems","date":"2025-02-18","arxiv_id":"2502.13256","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-in-smart-power-grids-with","title":"Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach","date":"2025-02-18","arxiv_id":"2502.15793","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistically-significant-k-nnad-by-selective","title":"Statistically Significant $k$NNAD by Selective Inference","date":"2025-02-18","arxiv_id":"2502.12978","repositories_listed":0,"syntology":null},{"url":null,"slug":"component-aware-unsupervised-logical-anomaly","title":"Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection","date":"2025-02-17","arxiv_id":"2502.11712","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-anomaly-detection-in-iomt-networks","title":"Enhanced Anomaly Detection in IoMT Networks using Ensemble AI Models on the CICIoMT2024 Dataset","date":"2025-02-17","arxiv_id":"2502.11854","repositories_listed":0,"syntology":null},{"url":null,"slug":"unifying-explainable-anomaly-detection-and","title":"Unifying Explainable Anomaly Detection and Root Cause Analysis in Dynamical Systems","date":"2025-02-17","arxiv_id":"2502.12086","repositories_listed":0,"syntology":null},{"url":null,"slug":"wrt-sam-foundation-model-driven-segmentation","title":"WRT-SAM: Foundation Model-Driven Segmentation for Generalized Weld Radiographic Testing","date":"2025-02-17","arxiv_id":"2502.11338","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-point-language-models-with-dual","title":"Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly Detection","date":"2025-02-16","arxiv_id":"2502.11307","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-anomaly-detection-with-topology","title":"Enhancing anomaly detection with topology-aware autoencoders","date":"2025-02-14","arxiv_id":"2502.10163","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-driven-cybersecurity","title":"Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy-Preserving and Real-Time Threat Detection Capabilities","date":"2025-02-14","arxiv_id":"2502.10599","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-hybrid-ensemble-model-for","title":"Privacy-Preserving Hybrid Ensemble Model for Network Anomaly Detection: Balancing Security and Data Protection","date":"2025-02-13","arxiv_id":"2502.09001","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-anomaly-detection-on-implicit","title":"Unsupervised Anomaly Detection on Implicit Shape representations for Sarcopenia Detection","date":"2025-02-13","arxiv_id":"2502.09088","repositories_listed":0,"syntology":null},{"url":null,"slug":"curvgad-leveraging-curvature-for-enhanced","title":"CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection","date":"2025-02-12","arxiv_id":"2502.08605","repositories_listed":0,"syntology":null},{"url":null,"slug":"genias-generator-for-instantiating-anomalies","title":"GenIAS: Generator for Instantiating Anomalies in time Series","date":"2025-02-12","arxiv_id":"2502.08262","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-climate-model-interpretability","title":"Advancing climate model interpretability: Feature attribution for Arctic melt anomalies","date":"2025-02-11","arxiv_id":"2502.07741","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-precision-oncology-through-modeling","title":"Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data","date":"2025-02-11","arxiv_id":"2502.07836","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-zero-shot-anomaly-detection-and","title":"Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models","date":"2025-02-11","arxiv_id":"2502.07601","repositories_listed":0,"syntology":null},{"url":null,"slug":"foundation-models-for-anomaly-detection","title":"Foundation Models for Anomaly Detection: Vision and Challenges","date":"2025-02-10","arxiv_id":"2502.06911","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-gpt-4o-efficiency-for-detecting","title":"Leveraging GPT-4o Efficiency for Detecting Rework Anomaly in Business Processes","date":"2025-02-10","arxiv_id":"2502.06918","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-task-representation-memory-bank-vs","title":"Multimodal Task Representation Memory Bank vs. Catastrophic Forgetting in Anomaly Detection","date":"2025-02-10","arxiv_id":"2502.06194","repositories_listed":0,"syntology":null},{"url":null,"slug":"safe-self-supervised-anomaly-detection","title":"SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection","date":"2025-02-10","arxiv_id":"2502.07119","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-copyright-protection-for-knowledge","title":"Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning","date":"2025-02-10","arxiv_id":"2502.10440","repositories_listed":0,"syntology":null},{"url":null,"slug":"aero-engines-anomaly-detection-using-an","title":"Aero-engines Anomaly Detection using an Unsupervised Fisher Autoencoder","date":"2025-02-08","arxiv_id":"2502.05428","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-masked-autoencoder-for","title":"Multi-scale Masked Autoencoder for Electrocardiogram Anomaly Detection","date":"2025-02-08","arxiv_id":"2502.05494","repositories_listed":0,"syntology":null},{"url":null,"slug":"dcformer-efficient-3d-vision-language","title":"DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions","date":"2025-02-07","arxiv_id":"2502.05091","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-for-anomaly-detection-in","title":"Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks","date":"2025-02-07","arxiv_id":"2502.05041","repositories_listed":0,"syntology":null},{"url":null,"slug":"finding-pegasus-enhancing-unsupervised","title":"Finding Pegasus: Enhancing Unsupervised Anomaly Detection in High-Dimensional Data using a Manifold-Based Approach","date":"2025-02-06","arxiv_id":"2502.04310","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-bedside-to-desktop-a-data-protocol-for","title":"From Bedside to Desktop: A Data Protocol for Normative Intracranial EEG and Abnormality Mapping","date":"2025-02-06","arxiv_id":"2502.04460","repositories_listed":0,"syntology":null},{"url":null,"slug":"position-untrained-machine-learning-for","title":"Position: Untrained Machine Learning for Anomaly Detection","date":"2025-02-06","arxiv_id":"2502.03876","repositories_listed":0,"syntology":null},{"url":null,"slug":"aero-llm-a-distributed-framework-for-secure","title":"Aero-LLM: A Distributed Framework for Secure UAV Communication and Intelligent Decision-Making","date":"2025-02-05","arxiv_id":"2502.05220","repositories_listed":0,"syntology":null},{"url":null,"slug":"calibrated-unsupervised-anomaly-detection-in","title":"Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning","date":"2025-02-05","arxiv_id":"2502.03245","repositories_listed":0,"syntology":null},{"url":"/paper/general-time-series-model-for-universal","slug":"general-time-series-model-for-universal","title":"General Time-series Model for Universal Knowledge Representation of Multivariate Time-Series data","date":"2025-02-05","arxiv_id":"2502.03264","repositories_listed":0,"syntology":{"n":12,"n_ran":8,"n_constructed":8,"n_ran_checked":8,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 8 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified; every one of the 8 samples that ran constructed an object rather than computing a result","sample_list":"/paper/general-time-series-model-for-universal#ran","syntology_url":"https://syntology.ai/paper/2502.03264","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.03264"}},"official":null}},{"url":null,"slug":"topocl-topological-contrastive-learning-for","title":"TopoCL: Topological Contrastive Learning for Time Series","date":"2025-02-05","arxiv_id":"2502.02924","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-via-autoencoder-composite","title":"Anomaly Detection via Autoencoder Composite Features and NCE","date":"2025-02-04","arxiv_id":"2502.01920","repositories_listed":0,"syntology":null},{"url":null,"slug":"complying-with-the-eu-ai-act-innovations-in","title":"Complying with the EU AI Act: Innovations in Explainable and User-Centric Hand Gesture Recognition","date":"2025-02-04","arxiv_id":"2503.15528","repositories_listed":0,"syntology":null},{"url":null,"slug":"last-stop-for-modeling-asynchronous-time","title":"LAST SToP For Modeling Asynchronous Time Series","date":"2025-02-04","arxiv_id":"2502.01922","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-poisson-process-autodecoder-for-x-ray","title":"A Poisson Process AutoDecoder for X-ray Sources","date":"2025-02-03","arxiv_id":"2502.01627","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditionnet-learning-preconditions-and","title":"ConditionNET: Learning Preconditions and Effects for Execution Monitoring","date":"2025-02-03","arxiv_id":"2502.01167","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-causality-for-enhanced-prediction-of","title":"Using Causality for Enhanced Prediction of Web Traffic Time Series","date":"2025-02-02","arxiv_id":"2502.00612","repositories_listed":0,"syntology":null},{"url":null,"slug":"predictive-modeling-and-anomaly-detection-in","title":"Predictive modeling and anomaly detection in large-scale web portals through the CAWAL framework","date":"2025-02-01","arxiv_id":"2502.00413","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-optimal-cascade-feature-level","title":"An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus","date":"2025-01-31","arxiv_id":"2501.18821","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-user-behavior-sequence-generation","title":"Synthetic User Behavior Sequence Generation with Large Language Models for Smart Homes","date":"2025-01-31","arxiv_id":"2501.19298","repositories_listed":0,"syntology":null},{"url":null,"slug":"battery-state-of-health-estimation-using-llm","title":"Battery State of Health Estimation Using LLM Framework","date":"2025-01-30","arxiv_id":"2501.18123","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-inference-real-time-anomaly-detection","title":"Real-Time Anomaly Detection with Synthetic Anomaly Monitoring (SAM)","date":"2025-01-30","arxiv_id":"2501.18417","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-anomalies-using-rotated-isolation","title":"Detecting Anomalies Using Rotated Isolation Forest","date":"2025-01-29","arxiv_id":"2501.17787","repositories_listed":0,"syntology":null},{"url":null,"slug":"koopagru-a-koopman-based-anomaly-detection-in","title":"KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units","date":"2025-01-29","arxiv_id":"2501.17976","repositories_listed":0,"syntology":null},{"url":null,"slug":"si4onnx-a-python-package-for-selective","title":"si4onnx: A Python package for Selective Inference in Deep Learning Models","date":"2025-01-29","arxiv_id":"2501.17415","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-web-service-anomaly-detection-via","title":"Enhancing Web Service Anomaly Detection via Fine-grained Multi-modal Association and Frequency Domain Analysis","date":"2025-01-28","arxiv_id":"2501.16875","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-for-efficient-condition","title":"Federated Learning for Efficient Condition Monitoring and Anomaly Detection in Industrial Cyber-Physical Systems","date":"2025-01-28","arxiv_id":"2501.16666","repositories_listed":0,"syntology":null},{"url":null,"slug":"llm-assisted-anomaly-detection-service-for","title":"LLM Assisted Anomaly Detection Service for Site Reliability Engineers: Enhancing Cloud Infrastructure Resilience","date":"2025-01-28","arxiv_id":"2501.16744","repositories_listed":0,"syntology":null},{"url":null,"slug":"maucell-an-adaptive-multi-attention-framework","title":"MAUCell: An Adaptive Multi-Attention Framework for Video Frame Prediction","date":"2025-01-28","arxiv_id":"2501.16997","repositories_listed":0,"syntology":null},{"url":"/paper/can-multimodal-large-language-models-be","slug":"can-multimodal-large-language-models-be","title":"Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?","date":"2025-01-27","arxiv_id":"2501.15795","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-transfer-learning-framework-for-anomaly-1","title":"A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data","date":"2025-01-26","arxiv_id":"2501.15365","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-client-selection-in-federated","title":"Adaptive Client Selection in Federated Learning: A Network Anomaly Detection Use Case","date":"2025-01-25","arxiv_id":"2501.15038","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-client-selection-in-federated","title":"Efficient Client Selection in Federated Learning","date":"2025-01-25","arxiv_id":"2502.00036","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-impact-of-optimised","title":"Exploring the impact of Optimised Hyperparameters on Bi-LSTM-based Contextual Anomaly Detector","date":"2025-01-25","arxiv_id":"2501.15053","repositories_listed":0,"syntology":null},{"url":null,"slug":"median-of-forests-for-robust-density","title":"Median of Forests for Robust Density Estimation","date":"2025-01-25","arxiv_id":"2501.15157","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-anomaly-detection-with","title":"Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs","date":"2025-01-25","arxiv_id":"2501.15035","repositories_listed":0,"syntology":null},{"url":null,"slug":"stones-from-other-hills-can-polish-jade-zero","title":"\"Stones from Other Hills can Polish Jade\": Zero-shot Anomaly Image Synthesis via Cross-domain Anomaly Injection","date":"2025-01-25","arxiv_id":"2501.15211","repositories_listed":0,"syntology":null},{"url":null,"slug":"argos-agentic-time-series-anomaly-detection","title":"Argos: Agentic Time-Series Anomaly Detection with Autonomous Rule Generation via Large Language Models","date":"2025-01-24","arxiv_id":"2501.14170","repositories_listed":0,"syntology":null},{"url":null,"slug":"bi-directional-curriculum-learning-for-graph","title":"Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity","date":"2025-01-24","arxiv_id":"2501.14197","repositories_listed":0,"syntology":null},{"url":null,"slug":"autoencoders-for-anomaly-detection-are","title":"Autoencoders for Anomaly Detection are Unreliable","date":"2025-01-23","arxiv_id":"2501.13864","repositories_listed":0,"syntology":null},{"url":null,"slug":"gcad-anomaly-detection-in-multivariate-time","title":"GCAD: Anomaly Detection in Multivariate Time Series from the Perspective of Granger Causality","date":"2025-01-23","arxiv_id":"2501.13493","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-digital-twin-and-machine-learning","title":"Leveraging Digital Twin and Machine Learning Techniques for Anomaly Detection in Power Electronics Dominated Grid","date":"2025-01-23","arxiv_id":"2501.13474","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-in-double-entry-bookkeeping","title":"Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach","date":"2025-01-22","arxiv_id":"2501.12723","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-window-based-detection-a-graph-centric","title":"Beyond Window-Based Detection: A Graph-Centric Framework for Discrete Log Anomaly Detection","date":"2025-01-21","arxiv_id":"2501.12166","repositories_listed":0,"syntology":null},{"url":null,"slug":"score-combining-for-contrastive-ood-detection","title":"Score Combining for Contrastive OOD Detection","date":"2025-01-21","arxiv_id":"2501.12204","repositories_listed":0,"syntology":null},{"url":null,"slug":"tad-bench-a-comprehensive-benchmark-for","title":"TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly Detection","date":"2025-01-21","arxiv_id":"2501.11960","repositories_listed":0,"syntology":null},{"url":null,"slug":"teacher-encoder-student-decoder-denoising","title":"Teacher Encoder-Student Decoder Denoising Guided Segmentation Network for Anomaly Detection","date":"2025-01-21","arxiv_id":"2501.12104","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-for-industrial-applications","title":"Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review","date":"2025-01-20","arxiv_id":"2501.11310","repositories_listed":0,"syntology":null},{"url":null,"slug":"class-imbalance-in-anomaly-detection-learning","title":"Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model","date":"2025-01-20","arxiv_id":"2501.11638","repositories_listed":0,"syntology":null},{"url":null,"slug":"complexvad-detecting-interaction-anomalies-in","title":"ComplexVAD: Detecting Interaction Anomalies in Video","date":"2025-01-16","arxiv_id":"2501.09733","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-patchcore-anomaly-detection-for","title":"Sequential PatchCore: Anomaly Detection for Surface Inspection using Synthetic Impurities","date":"2025-01-16","arxiv_id":"2501.09579","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-based-multivariate-time-series","title":"Transformer-based Multivariate Time Series Anomaly Localization","date":"2025-01-15","arxiv_id":"2501.08628","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-contextual-anomalies-by-discovering","title":"Detecting Contextual Anomalies by Discovering Consistent Spatial Regions","date":"2025-01-14","arxiv_id":"2501.08470","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-input-variational-auto-encoder-for","title":"Multiple-Input Variational Auto-Encoder for Anomaly Detection in Heterogeneous Data","date":"2025-01-14","arxiv_id":"2501.08149","repositories_listed":0,"syntology":null},{"url":null,"slug":"stts-ead-improving-spatio-temporal-learning","title":"STTS-EAD: Improving Spatio-Temporal Learning Based Time Series Prediction via","date":"2025-01-14","arxiv_id":"2501.07814","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-feature-construction-for-anomaly","title":"Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation","date":"2025-01-14","arxiv_id":"2501.07999","repositories_listed":0,"syntology":null},{"url":null,"slug":"driver-age-and-its-effect-on-key-driving","title":"Driver Age and Its Effect on Key Driving Metrics: Insights from Dynamic Vehicle Data","date":"2025-01-12","arxiv_id":"2501.06918","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-ai-enabled-robust-sensor-placement","title":"Generative AI Enabled Robust Sensor Placement in Cyber-Physical Power Systems: A Graph Diffusion Approach","date":"2025-01-12","arxiv_id":"2501.06756","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-rule-mining-for-multivariate-anomaly","title":"Active Rule Mining for Multivariate Anomaly Detection in Radio Access Networks","date":"2025-01-11","arxiv_id":"2501.06571","repositories_listed":0,"syntology":null},{"url":null,"slug":"explaining-deep-learning-based-anomaly","title":"Explaining Deep Learning-based Anomaly Detection in Energy Consumption Data by Focusing on Contextually Relevant Data","date":"2025-01-10","arxiv_id":"2501.06099","repositories_listed":0,"syntology":null},{"url":null,"slug":"facilitate-collaboration-between-large","title":"Synergizing Large Language Models and Task-specific Models for Time Series Anomaly Detection","date":"2025-01-10","arxiv_id":"2501.05675","repositories_listed":0,"syntology":null},{"url":null,"slug":"eva-s2plor-a-secure-element-wise","title":"EVA-S2PLoR: A Secure Element-wise Multiplication Meets Logistic Regression on Heterogeneous Database","date":"2025-01-09","arxiv_id":"2501.05223","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-in-distribution-representations-for","title":"Learning Compact and Robust Representations for Anomaly Detection","date":"2025-01-09","arxiv_id":"2501.05130","repositories_listed":0,"syntology":null},{"url":null,"slug":"back-home-a-machine-learning-approach-to","title":"Back Home: A Machine Learning Approach to Seashell Classification and Ecosystem Restoration","date":"2025-01-08","arxiv_id":"2501.04873","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-registers-in-vision-transformers","title":"Leveraging Registers in Vision Transformers for Robust Adaptation","date":"2025-01-08","arxiv_id":"2501.04784","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-hybrid-support-vector-machines-for","title":"Quantum Hybrid Support Vector Machines for Stress Detection in Older Adults","date":"2025-01-08","arxiv_id":"2501.04831","repositories_listed":0,"syntology":null},{"url":"/paper/kanoclip-zero-shot-anomaly-detection-through","slug":"kanoclip-zero-shot-anomaly-detection-through","title":"KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration","date":"2025-01-07","arxiv_id":"2501.03786","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphdart-graph-distillation-for-efficient","title":"GraphDART: Graph Distillation for Efficient Advanced Persistent Threat Detection","date":"2025-01-06","arxiv_id":"2501.02796","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-adversarial-robustness-of-benjamini","title":"On the Adversarial Robustness of Benjamini Hochberg","date":"2025-01-06","arxiv_id":"2501.03402","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-tomato-split-anomaly-detection","title":"Unsupervised Tomato Split Anomaly Detection using Hyperspectral Imaging and Variational Autoencoders","date":"2025-01-06","arxiv_id":"2501.02921","repositories_listed":0,"syntology":null},{"url":null,"slug":"counterfactual-explanation-for-auto-encoder","title":"Counterfactual Explanation for Auto-Encoder Based Time-Series Anomaly Detection","date":"2025-01-03","arxiv_id":"2501.02069","repositories_listed":0,"syntology":null},{"url":null,"slug":"logicad-explainable-anomaly-detection-via-vlm","title":"LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction","date":"2025-01-03","arxiv_id":"2501.01767","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-outlier-detection-algorithm-for","title":"An Efficient Outlier Detection Algorithm for Data Streaming","date":"2025-01-02","arxiv_id":"2501.01061","repositories_listed":0,"syntology":null}],"record_sha256":"b3e099b609a978ca652e9d149536f64922aa21416727ebfff62be1a8a2ccd169","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}