SOTAVerified

Outlier Detection

Outlier Detection is a task of identifying a subset of a given data set which are considered anomalous in that they are unusual from other instances. It is one of the core data mining tasks and is central to many applications. In the security field, it can be used to identify potentially threatening users, in the manufacturing field it can be used to identify parts that are likely to fail.

Source: Coverage-based Outlier Explanation

Papers

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Showing 326–350 of 703 papers

TitleStatusHype
Evaluating the Efficacy of Foundational Models: Advancing Benchmarking Practices to Enhance Fine-Tuning Decision-Making—0
Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data—0
Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles—0
Hyperbolic Metric Learning for Visual Outlier Detection—0
Capturing the Denoising Effect of PCA via Compression Ratio—0
ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitment—0
Identifying Informative Latent Variables Learned by GIN via Mutual Information—0
Identifying Outlier Arms in Multi-Armed Bandit—0
Identifying Semantically Deviating Outlier Documents—0
Explainable and Robust Millimeter Wave Beam Alignment for AI-Native 6G Networks—0
Byzantine-Resilient Secure Federated Learning—0
Image Modeling with Deep Convolutional Gaussian Mixture Models—0
Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuation—0
Impacts of the Numbers of Colors and Shapes on Outlier Detection: from Automated to User Evaluation—0
Conditional Testing based on Localized Conformal p-values—0
A Practical Algorithm for Distributed Clustering and Outlier Detection—0
Improving Solar Flare Prediction by Time Series Outlier Detection—0
A Bayesian Ensemble for Unsupervised Anomaly Detection—0
Incremental Outlier Detection Modelling Using Streaming Analytics in Finance & Health Care—0
Kernel Random Projection Depth for Outlier Detection—0
Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach—0
Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation—0
Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies—0
Breakdown Point of Robust Support Vector Machine—0
Anomaly Detection for an E-commerce Pricing System—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1VRAE+SVMAccuracy0.98—Unverified
2F-t ALSTM-FCNAccuracy0.95—Unverified
3GENDISAccuracy0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.03—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy37.62—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy65.6—Unverified
#ModelMetricClaimedVerifiedStatus
1PAEAUROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.05—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC0.86—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy86.33—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTMCapsAverage F10.74—Unverified