Outlier Detection
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Benchmarks
ECG5000
Balance scale_class 1
Fashion-MNIST
Glass identification
Heart-C
Hepatitis
Internet Ad
Ionosphere_class b
SKAB
Most implemented
Towards Total Recall in Industrial Anomaly Detection
LSTM Fully Convolutional Networks for Time Series Classification
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
Deep Semi-Supervised Anomaly Detection
Deep Sets
ADBench: Anomaly Detection Benchmark
Papers
Efficient Estimation of High Information Projections using Nearest Neighbours
An intuitive method for dimensionality reduction is proposed, which is highly effective for finding interesting projections of multivariate data. Following similar intuitive motivation to a number of existing techniques,…
Dimensionality ReductionOutlier DetectionImproving Energy Efficiency of Oil Platforms Through Optimal Loading of Diesel Generators Using Machine Learning and Search Algorithms
Rising energy demand, fossil fuel depletion and climate change highlight the need for more efficient energy production and consumption. Offshore oil and gas platforms face challenges related to inefficient energy use, sy…
Outlier DetectionVideoRun2D Demo: Markerless Body Tracking for Biomechanical Analysis of Running
Human pose estimation has advanced significantly due to the development of deep learning models, increased data availability, and improved computing resources. These developments have led to highly accurate body tracking…
Outlier DetectionPose EstimationWasserstein Filtering: A Sample Selection Method for Robust Distribution Learning
Given a dataset where a portion of the samples are contaminated, our goal is to recover the underlying clean population distribution. To this end, we propose Wasserstein Filtering (WF), a novel sample selection framework…
Outlier DetectionAnomaly DetectionGeneralised Robust Bayes for Joint Inference of Model and Contamination
Generalised Bayesian inference (GBI) has emerged as a compelling robust alternative to standard Bayesian inference, mitigating sensitivity to data contamination by replacing the log-likelihood with a robust loss or diver…
Bayesian InferenceOutlier DetectionContaminated Multi-task Learning with Heterogeneity: Fundamental Limits and Optimal Algorithms
Integrating information across related tasks can improve estimation and prediction in transfer, multi-task, and federated learning, but contamination and heterogeneity make robust borrowing challenging. We study a contam…
Multi-Task LearningFederated LearningOutlier Detection