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Outlier Detection
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Universal Embeddings of Tabular Data
Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers
LayerIF: Estimating Layer Quality for Large Language Models using Influence Functions
Learning novel representations of variable sources from multi-modal $\textit{Gaia}$ data via autoencoders
Re-experiment Smart: a Novel Method to Enhance Data-driven Prediction of Mechanical Properties of Epoxy Polymers
Importance Sampling for Nonlinear Models
Robust Indoor Localization via Conformal Methods and Variational Bayesian Adaptive Filtering
Benign Samples Matter! Fine-tuning On Outlier Benign Samples Severely Breaks Safety
Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach
Comparison of Visual Trackers for Biomechanical Analysis of Running
Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest
A probabilistic view on Riemannian machine learning models for SPD matrices
Unsupervised outlier detection to improve bird audio dataset labels
Adaptive PCA-Based Outlier Detection for Multi-Feature Time Series in Space Missions
Adversarial Subspace Generation for Outlier Detection in High-Dimensional Data
Robust Randomized Low-Rank Approximation with Row-Wise Outlier Detection
Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies
TRIDIS: A Comprehensive Medieval and Early Modern Corpus for HTR and NER
PS-EIP: Robust Photometric Stereo Based on Event Interval Profile
Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization
Highly Efficient Direct Analytics on Semantic-aware Time Series Data Compression
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model
OuroMamba: A Data-Free Quantization Framework for Vision Mamba Models
Cascade of one-class classifier ensemble and dynamic naive Bayes classifier applied to the myoelectric-based upper limb prosthesis control with contaminated channels detection
Robust Multi-Source Domain Adaptation under Label Shift
Out-of-Distribution Detection on Graphs: A Survey
Robust Conformal Outlier Detection under Contaminated Reference Data
CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learning
RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples
Explainable and Robust Millimeter Wave Beam Alignment for AI-Native 6G Networks
Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model
Data Enrichment Opportunities for Distribution Grid Cable Networks using Variational Autoencoders
Temporal Analysis of Adversarial Attacks in Federated Learning
Outlyingness Scores with Cluster Catch Digraphs
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