{"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/46","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":46,"pages_in_order":49,"rows_per_page":100,"rows":[4501,4600],"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/45","next":"/task/anomaly-detection/papers/47","papers":[{"url":null,"slug":"ads-me-anomaly-detection-system-for-micro","title":"ADS-ME: Anomaly Detection System for Micro-expression Spotting","date":"2019-03-11","arxiv_id":"1903.04354","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-design-exploration-by-integrating","title":"Deep Generative Design: Integration of Topology Optimization and Generative Models","date":"2019-03-01","arxiv_id":"1903.01548","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-abnormality-detection-through","title":"Unsupervised Abnormality Detection through Mixed Structure Regularization (MSR) in Deep Sparse Autoencoders","date":"2019-02-28","arxiv_id":"1902.11036","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-for-an-e-commerce-pricing","title":"Anomaly Detection for an E-commerce Pricing System","date":"2019-02-25","arxiv_id":"1902.09566","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-corner-case-detection-for-autonomous","title":"Towards Corner Case Detection for Autonomous Driving","date":"2019-02-25","arxiv_id":"1902.09184","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-anomaly-detection-and-classification","title":"Bayesian Anomaly Detection and Classification","date":"2019-02-22","arxiv_id":"1902.08627","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-pcg-anomaly-detection-by-adaptive","title":"Real-time PCG Anomaly Detection by Adaptive 1D Convolutional Neural Networks","date":"2019-02-19","arxiv_id":"1902.07238","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-one-class-support-vector-machine","title":"A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection","date":"2019-02-18","arxiv_id":"1902.06361","repositories_listed":0,"syntology":null},{"url":null,"slug":"kinn-incorporating-expert-knowledge-in-neural","title":"KINN: Incorporating Expert Knowledge in Neural Networks","date":"2019-02-15","arxiv_id":"1902.05653","repositories_listed":0,"syntology":null},{"url":"/paper/street-scene-a-new-dataset-and-evaluation","slug":"street-scene-a-new-dataset-and-evaluation","title":"Street Scene: A new dataset and evaluation protocol for video anomaly detection","date":"2019-02-15","arxiv_id":"1902.05872","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-probabilistic-framework-to-node-level","title":"A Probabilistic Framework to Node-level Anomaly Detection in Communication Networks","date":"2019-02-12","arxiv_id":"1902.04521","repositories_listed":0,"syntology":null},{"url":null,"slug":"bounded-fuzzy-possibilistic-method","title":"Bounded Fuzzy Possibilistic Method","date":"2019-02-08","arxiv_id":"1902.03127","repositories_listed":0,"syntology":null},{"url":null,"slug":"dictionary-learning-approach-to-monitoring-of","title":"Dictionary learning approach to monitoring of wind turbine drivetrain bearings","date":"2019-02-04","arxiv_id":"1902.01426","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-prediction-of-negative-health","title":"Unsupervised Prediction of Negative Health Events Ahead of Time","date":"2019-01-31","arxiv_id":"1901.11168","repositories_listed":0,"syntology":null},{"url":null,"slug":"securing-fog-to-things-environment-using","title":"Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning","date":"2019-01-30","arxiv_id":"1901.10933","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-locality-in-video-surveillance","title":"Anomaly Locality in Video Surveillance","date":"2019-01-29","arxiv_id":"1901.10364","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalization-of-feature-embeddings","title":"Generalization of feature embeddings transferred from different video anomaly detection domains","date":"2019-01-28","arxiv_id":"1901.09819","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-in-road-traffic-using","title":"Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey","date":"2019-01-24","arxiv_id":"1901.08292","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-anomaly-detection-in-images-using","title":"Robust Anomaly Detection in Images using Adversarial Autoencoders","date":"2019-01-18","arxiv_id":"1901.06355","repositories_listed":0,"syntology":null},{"url":null,"slug":"background-subtraction-on-depth-videos-with","title":"Background subtraction on depth videos with convolutional neural networks","date":"2019-01-17","arxiv_id":"1901.05676","repositories_listed":0,"syntology":null},{"url":null,"slug":"cfof-a-concentration-free-measure-for-anomaly","title":"CFOF: A Concentration Free Measure for Anomaly Detection","date":"2019-01-14","arxiv_id":"1901.04992","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-machine-synesthetic-approach-to-ddos","title":"A Machine-Synesthetic Approach To DDoS Network Attack Detection","date":"2019-01-13","arxiv_id":"1901.04017","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-pseudo-healthy-synthesis-needs","title":"Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization","date":"2019-01-10","arxiv_id":"1901.07295","repositories_listed":0,"syntology":null},{"url":null,"slug":"natively-interpretable-machine-learning-and","title":"Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions","date":"2019-01-02","arxiv_id":"1901.00246","repositories_listed":0,"syntology":null},{"url":null,"slug":"feedforward-neural-network-for-time-series","title":"Feedforward Neural Network for Time Series Anomaly Detection","date":"2018-12-20","arxiv_id":"1812.08389","repositories_listed":0,"syntology":null},{"url":null,"slug":"correlated-anomaly-detection-from-large","title":"Correlated Anomaly Detection from Large Streaming Data","date":"2018-12-19","arxiv_id":"1812.09387","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-and-interpretation-using","title":"Anomaly Detection and Interpretation using Multimodal Autoencoder and Sparse Optimization","date":"2018-12-18","arxiv_id":"1812.07136","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-anomaly-detection-in-energy-time","title":"Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention","date":"2018-12-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adaflow-domain-adaptive-density-estimator","title":"AdaFlow: Domain-Adaptive Density Estimator with Application to Anomaly Detection and Unpaired Cross-Domain Translation","date":"2018-12-14","arxiv_id":"1812.05796","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-encoding-variational-autoencoder-for","title":"Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection","date":"2018-12-14","arxiv_id":"1812.05941","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-anomaly-detection-using","title":"Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT","date":"2018-12-12","arxiv_id":"1812.04872","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-anomaly-detection-with-hmof-feature","title":"Real-Time Anomaly Detection With HMOF Feature","date":"2018-12-12","arxiv_id":"1812.04980","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-generation-using-generative","title":"Anomaly Generation using Generative Adversarial Networks in Host Based Intrusion Detection","date":"2018-12-11","arxiv_id":"1812.04697","repositories_listed":0,"syntology":null},{"url":null,"slug":"use-dimensionality-reduction-and-svm-methods","title":"Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks","date":"2018-12-07","arxiv_id":"1812.03173","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-with-wasserstein-gan","title":"Anomaly detection with Wasserstein GAN","date":"2018-12-06","arxiv_id":"1812.02463","repositories_listed":0,"syntology":null},{"url":null,"slug":"cyber-anomaly-detection-using-graph-node-role","title":"Cyber Anomaly Detection Using Graph-node Role-dynamics","date":"2018-12-06","arxiv_id":"1812.02848","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-for-network-connection-logs","title":"Anomaly Detection for Network Connection Logs","date":"2018-12-01","arxiv_id":"1812.01941","repositories_listed":0,"syntology":null},{"url":null,"slug":"adsas-comprehensive-real-time-anomaly","title":"ADSaS: Comprehensive Real-time Anomaly Detection System","date":"2018-11-30","arxiv_id":"1811.12634","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-models-for-iot-time-series","title":"Anomaly Detection Models for IoT Time Series Data","date":"2018-11-30","arxiv_id":"1812.00890","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-machine-learning-phase-classification","title":"A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics","date":"2018-11-29","arxiv_id":"1811.12119","repositories_listed":0,"syntology":null},{"url":null,"slug":"class-augmented-semi-supervised-learning-for","title":"Class Augmented Semi-Supervised Learning for Practical Clinical Analytics on Physiological Signals","date":"2018-11-29","arxiv_id":"1812.07498","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-state-representations-in-complex","title":"Learning State Representations in Complex Systems with Multimodal Data","date":"2018-11-27","arxiv_id":"1811.11067","repositories_listed":0,"syntology":null},{"url":null,"slug":"attentioned-convolutional-lstm","title":"Attentioned Convolutional LSTM InpaintingNetwork for Anomaly Detection in Videos","date":"2018-11-26","arxiv_id":"1811.10228","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-pre-trained-cnns-good-feature-extractors","title":"Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?","date":"2018-11-20","arxiv_id":"1811.08495","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-graphs-for-sensor-data-driven","title":"Probabilistic Graphs for Sensor Data-driven Modelling of Power Systems at Scale","date":"2018-11-18","arxiv_id":"1811.07267","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-using-deep-learning-based","title":"Anomaly Detection using Deep Learning based Image Completion","date":"2018-11-16","arxiv_id":"1811.06861","repositories_listed":0,"syntology":null},{"url":"/paper/unexpected-item-in-the-bagging-area-anomaly","slug":"unexpected-item-in-the-bagging-area-anomaly","title":"‘Unexpected item in the bagging area’: Anomaly Detection in X-ray Security Images","date":"2018-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-trace-criterion-for-kernel-bandwidth","title":"The Trace Criterion for Kernel Bandwidth Selection for Support Vector Data Description","date":"2018-11-15","arxiv_id":"1811.06838","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-learning-based-on-line-anomaly","title":"Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy","date":"2018-11-12","arxiv_id":"1811.04539","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimation-of-dimensions-contributing-to","title":"Estimation of Dimensions Contributing to Detected Anomalies with Variational Autoencoders","date":"2018-11-12","arxiv_id":"1811.04576","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-for-imbalanced-datasets","title":"Anomaly Detection for imbalanced datasets with Deep Generative Models","date":"2018-11-02","arxiv_id":"1811.00986","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-profiles-sensor-based-monitoring-and","title":"Multiple profiles sensor-based monitoring and anomaly detection","date":"2018-10-31","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adepos-anomaly-detection-based-power-saving","title":"ADEPOS: Anomaly Detection based Power Saving for Predictive Maintenance using Edge Computing","date":"2018-10-30","arxiv_id":"1811.00873","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-anomaly-detection-with-switching-cost","title":"Active Anomaly Detection with Switching Cost","date":"2018-10-28","arxiv_id":"1810.11800","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-anomaly-detection-and","title":"Machine Learning for Anomaly Detection and Categorization in Multi-cloud Environments","date":"2018-10-23","arxiv_id":"1812.05443","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-stacked-autoencoder-neural-network-based","title":"A Stacked Autoencoder Neural Network based Automated Feature Extraction Method for Anomaly detection in On-line Condition Monitoring","date":"2018-10-19","arxiv_id":"1810.08609","repositories_listed":0,"syntology":null},{"url":null,"slug":"qanet-tensor-decomposition-approach-for-query","title":"QANet: Tensor Decomposition Approach for Query-based Anomaly Detection in Heterogeneous Information Networks","date":"2018-10-19","arxiv_id":"1810.08382","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-anomalous-data-space","title":"Unsupervised Anomalous Data Space Specification","date":"2018-10-18","arxiv_id":"1810.08309","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-roadmap-towards-resilient-internet-of","title":"A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems","date":"2018-10-16","arxiv_id":"1810.06870","repositories_listed":0,"syntology":null},{"url":null,"slug":"mdgan-boosting-anomaly-detection-using-multi","title":"MDGAN: Boosting Anomaly Detection Using \\\\Multi-Discriminator Generative Adversarial Networks","date":"2018-10-11","arxiv_id":"1810.05221","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-expert-system-for-anomaly-detection","title":"Real time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras","date":"2018-10-09","arxiv_id":"1810.04108","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-optimization-in-wireless-sensor","title":"Distributed optimization in wireless sensor networks: an island-model framework","date":"2018-10-05","arxiv_id":"1810.02679","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatially-weighted-anomaly-detection","title":"Spatially-weighted Anomaly Detection","date":"2018-10-05","arxiv_id":"1810.02607","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-based-anomaly-detection-for","title":"Clustering-based Anomaly Detection for microservices","date":"2018-10-04","arxiv_id":"1810.02762","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-sentence-parallelism-using","title":"Measuring sentence parallelism using Mahalanobis distances: The NRC unsupervised submissions to the WMT18 Parallel Corpus Filtering shared task","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-profiling-machine-active-generalization","title":"The Profiling Machine: Active Generalization over Knowledge","date":"2018-10-01","arxiv_id":"1810.00782","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-surveillance-technologies-for","title":"Interactive Surveillance Technologies for Dense Crowds","date":"2018-09-27","arxiv_id":"1810.03965","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-abnormality-detection-for-automatic","title":"Multiple Abnormality Detection for Automatic Medical Image Diagnosis Using Bifurcated Convolutional Neural Network","date":"2018-09-16","arxiv_id":"1809.05831","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-virtual-testbed-for-critical-incident","title":"A Virtual Testbed for Critical Incident Investigation with Autonomous Remote Aerial Vehicle Surveying, Artificial Intelligence, and Decision Support","date":"2018-09-14","arxiv_id":"1809.06244","repositories_listed":0,"syntology":null},{"url":null,"slug":"identification-of-temporal-transition-of","title":"Identification of temporal transition of functional states using recurrent neural networks from functional MRI","date":"2018-09-14","arxiv_id":"1809.05560","repositories_listed":0,"syntology":null},{"url":null,"slug":"layerwise-perturbation-based-adversarial","title":"Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction","date":"2018-09-11","arxiv_id":"1809.04188","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-graph-auto-encoder-a-deep","title":"Convolutional Graph Auto-encoder: A Deep Generative Neural Architecture for Probabilistic Spatio-temporal Solar Irradiance Forecasting","date":"2018-09-10","arxiv_id":"1809.03538","repositories_listed":0,"syntology":null},{"url":null,"slug":"does-your-phone-know-your-touch","title":"Does Your Phone Know Your Touch?","date":"2018-09-10","arxiv_id":"1809.03402","repositories_listed":0,"syntology":null},{"url":null,"slug":"coupled-igmm-gans-for-deep-multimodal-anomaly","title":"Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data","date":"2018-09-08","arxiv_id":"1809.02728","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-level-hypothesis-testing-for","title":"Multi-level hypothesis testing for populations of heterogeneous networks","date":"2018-09-07","arxiv_id":"1809.02512","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-in-the-presence-of-missing","title":"Anomaly Detection in the Presence of Missing Values","date":"2018-09-05","arxiv_id":"1809.01605","repositories_listed":0,"syntology":null},{"url":"/paper/an-open-access-database-for-evaluating-the","slug":"an-open-access-database-for-evaluating-the","title":"An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"aad-adaptive-anomaly-detection-through","title":"AAD: Adaptive Anomaly Detection through traffic surveillance videos","date":"2018-08-29","arxiv_id":"1808.10044","repositories_listed":0,"syntology":null},{"url":null,"slug":"doping-generative-data-augmentation-for","title":"DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN","date":"2018-08-23","arxiv_id":"1808.07632","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-network-anomaly-detection-based-on","title":"Enhanced network anomaly detection based on deep neural networks","date":"2018-08-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neuromorphic-architecture-for-the","title":"Neuromorphic Architecture for the Hierarchical Temporal Memory","date":"2018-08-17","arxiv_id":"1808.05839","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-anomalies-a-review-and-synthesis-of","title":"Image Anomalies: a Review and Synthesis of Detection Methods","date":"2018-08-07","arxiv_id":"1808.02564","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-spectral-filtering-and-anomaly","title":"Robust Spectral Filtering and Anomaly Detection","date":"2018-08-03","arxiv_id":"1808.01181","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-via-minimum-likelihood","title":"Anomaly Detection via Minimum Likelihood Generative Adversarial Networks","date":"2018-08-01","arxiv_id":"1808.00200","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-multi-task-gaussian-process-tensor","title":"Scalable Multi-Task Gaussian Process Tensor Regression for Normative Modeling of Structured Variation in Neuroimaging Data","date":"2018-07-31","arxiv_id":"1808.00036","repositories_listed":0,"syntology":null},{"url":null,"slug":"call-detail-records-driven-anomaly-detection","title":"Call Detail Records Driven Anomaly Detection and Traffic Prediction in Mobile Cellular Networks","date":"2018-07-30","arxiv_id":"1807.11545","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-in-static-networks-using","title":"Anomaly detection in static networks using egonets","date":"2018-07-24","arxiv_id":"1807.08925","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-bayesian-density-analysis","title":"Automatic Bayesian Density Analysis","date":"2018-07-24","arxiv_id":"1807.09306","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-for-water-treatment-system","title":"Anomaly Detection for Water Treatment System based on Neural Network with Automatic Architecture Optimization","date":"2018-07-19","arxiv_id":"1807.07282","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-rnn-encoder-decoder-models-for","title":"Comparison of RNN Encoder-Decoder Models for Anomaly Detection","date":"2018-07-17","arxiv_id":"1807.06576","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-generative-model-using-unregularized","title":"Deep Generative Model using Unregularized Score for Anomaly Detection with Heterogeneous Complexity","date":"2018-07-16","arxiv_id":"1807.05800","repositories_listed":0,"syntology":null},{"url":null,"slug":"process-monitoring-using-maximum-sequence","title":"Process Monitoring Using Maximum Sequence Divergence","date":"2018-07-09","arxiv_id":"1807.03387","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-for-skin-disease-images","title":"Anomaly Detection for Skin Disease Images Using Variational Autoencoder","date":"2018-07-03","arxiv_id":"1807.01349","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-using-gans-for-visual","title":"Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data","date":"2018-07-03","arxiv_id":"1807.01136","repositories_listed":0,"syntology":null},{"url":null,"slug":"client-specific-anomaly-detection-for-face","title":"Client-Specific Anomaly Detection for Face Presentation Attack Detection","date":"2018-07-02","arxiv_id":"1807.00848","repositories_listed":0,"syntology":null},{"url":null,"slug":"distilling-the-posterior-in-bayesian-neural","title":"Distilling the Posterior in Bayesian Neural Networks","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"successive-convex-approximation-algorithms","title":"Successive Convex Approximation Algorithms for Sparse Signal Estimation with Nonconvex Regularizations","date":"2018-06-28","arxiv_id":"1806.10773","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-cyberattacks-in-industrial-control","title":"Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks","date":"2018-06-21","arxiv_id":"1806.08110","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-zero-day-controller-hijacking","title":"Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning","date":"2018-06-18","arxiv_id":"1806.06496","repositories_listed":0,"syntology":null},{"url":null,"slug":"cardiac-motion-scoring-with-segment-and","title":"Cardiac Motion Scoring with Segment- and Subject-level Non-Local Modeling","date":"2018-06-14","arxiv_id":"1806.05569","repositories_listed":0,"syntology":null}],"record_sha256":"8874b9fbacf9e228707ace31225da94acff24130a02a23c4abba5cb37c79f18e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}