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Anomaly Detection

132 papers

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Papers

Mitigating Overconfidence in Out-of-Distribution Detection by Capturing Extreme Activations UAI 2024 Towards Optimal Feature-Shaping Methods for Out-of-Distribution Detection ICLR 2024 Transductive and Inductive Outlier Detection with Robust Autoencoders UAI 2024 AE-FLOW: Autoencoders with Normalizing Flows for Medical Images Anomaly Detection ICLR 2023 BotPercent: Estimating Bot Populations in Twitter Communities EMNLP 2023 Decouple then Combine: A Simple and Effective Framework for Fraud Transaction Detection ACML 2023 Enhancing Global Network Monitoring with Magnifier NSDI 2023 Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics ICLR 2023 Unsupervised Model Selection for Time Series Anomaly Detection ICLR 2023 VTaC: A Benchmark Dataset of Ventricular Tachycardia Alarms from ICU Monitors NeurIPS 2023 What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization NeurIPS 2023 Evolutive Adversarially-Trained Bayesian Network Autoencoder for Interpretable Anomaly Detection PGM 2022 Collaborative Novelty Detection for Distributed Data by a Probabilistic Method ACML 2021 Estimating informativeness of samples with Smooth Unique Information ICLR 2021 MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection CVPR 2021 Adaptive Double-Exploration Tradeoff for Outlier Detection AAAI 2020 Interactive Anomaly Detection in Mixed Tabular Data using Bayesian Networks PGM 2020 Towards a Hierarchical Bayesian Model of Multi-View Anomaly Detection IJCAI 2020 An Encoding Adversarial Network for Anomaly Detection ACML 2019 Forward-Backward Generative Adversarial Networks for Anomaly Detection ACML 2019 Outlier Detection for Time Series with Recurrent Autoencoder Ensembles IJCAI 2019 Efficient Minimax Signal Detection on Graphs NeurIPS 2014 Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic NeurIPS 2013 Rapid Distance-Based Outlier Detection via Sampling NeurIPS 2013 Locating Changes in Highly Dependent Data with Unknown Number of Change Points NeurIPS 2012