A rule-based strikingness measure is added to TKGR metrics to weight rare events higher, revealing that models weaken on striking events and ensemble gains come mostly from trivial fits.
Deep learning for anomaly detection
8 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
verdicts
UNVERDICTED 8representative citing papers
uLEAD-TabPFN detects anomalies in tabular data by scoring violations of conditional dependencies estimated via frozen PFNs with uncertainty awareness, achieving top average rank and up to 20% ROC-AUC gains on high-dimensional ADBench datasets.
Diffusion models via DDPM work for anomaly detection but are slow; the proposed DTE method estimates diffusion time distribution analytically and with a neural net to deliver faster inference while outperforming DDPM on ADBench for unsupervised and semi-supervised settings.
A single fixed term in the Shapley value yields the same anomaly localization error probability as the full calculation for independent sensor observations, supported by a proof.
Tensor Train compression algorithms detect anomalies by maintaining normal data structure and deleting anomalous structure, tested on digits, faces, and cyber-attack datasets.
Proposes the TRIAD framework that treats multi-turn multimodal attacks as continuous trajectories and uses structural anomaly detection, regularized Mahalanobis distance, topological acceleration, and a time-varying Cox model with Bayesian HMM feedback to predict and bound expected time-to-failure.
ESN-DAGMM adapts DAGMM with an ESN layer for temporal modeling and reports 269.59% better average clustering quality than baselines on 10% of an O-RAN video-streaming dataset.
Genetic algorithm feature selection with Extra Trees reduces PMU features from 112 to an average of 27.4 while raising macro-F1 from 0.9118 to 0.9212 and ROC-AUC from 0.9791 to 0.9837 on the MSU/ORNL dataset.
citing papers explorer
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Strikingness-Aware Evaluation for Temporal Knowledge Graph Reasoning
A rule-based strikingness measure is added to TKGR metrics to weight rare events higher, revealing that models weaken on striking events and ensemble gains come mostly from trivial fits.
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uLEAD-TabPFN: Uncertainty-aware Dependency-based Anomaly Detection with TabPFN
uLEAD-TabPFN detects anomalies in tabular data by scoring violations of conditional dependencies estimated via frozen PFNs with uncertainty awareness, achieving top average rank and up to 20% ROC-AUC gains on high-dimensional ADBench datasets.
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On Diffusion Modeling for Anomaly Detection
Diffusion models via DDPM work for anomaly detection but are slow; the proposed DTE method estimates diffusion time distribution analytically and with a neural net to deliver faster inference while outperforming DDPM on ADBench for unsupervised and semi-supervised settings.
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On Using the Shapley Value for Anomaly Localization: A Statistical Investigation
A single fixed term in the Shapley value yields the same anomaly localization error probability as the full calculation for independent sensor observations, supported by a proof.
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Anomaly Detection from a Tensor Train Perspective
Tensor Train compression algorithms detect anomalies by maintaining normal data structure and deleting anomalous structure, tested on digits, faces, and cyber-attack datasets.
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Surviving the Unseen: Predictive Defense for Novel Multi-Turn Multimodal Attacks
Proposes the TRIAD framework that treats multi-turn multimodal attacks as continuous trajectories and uses structural anomaly detection, regularized Mahalanobis distance, topological acceleration, and a time-varying Cox model with Bayesian HMM feedback to predict and bound expected time-to-failure.
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ESN-DAGMM: A Lightweight Framework for Unsupervised Time-Series Data Monitoring in 5G O-RAN Networks
ESN-DAGMM adapts DAGMM with an ESN layer for temporal modeling and reports 269.59% better average clustering quality than baselines on 10% of an O-RAN video-streaming dataset.
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Cyber-Physical Anomaly Detection in IoT-Enabled Smart Grids Using Machine Learning and Metaheuristic Feature Optimization
Genetic algorithm feature selection with Extra Trees reduces PMU features from 112 to an average of 27.4 while raising macro-F1 from 0.9118 to 0.9212 and ROC-AUC from 0.9791 to 0.9837 on the MSU/ORNL dataset.