A soft harmonic function approach estimates label confidence for conditional anomaly detection while regularizing against isolated examples and distribution boundaries.
Anomaly detection: A survey
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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.
A new catalog classifying 35 data error types into missing, incorrect, and redundant categories for tabular data, with definitions and examples to improve data quality management.
An automated Python simulator, calibrated to one experimental run, generates consistent time-series data for many batch distillation scenarios including anomalies, forming an openly released hybrid dataset for deep 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.
A systematic review that introduces a framework for feature extraction in remote sensing, traces its evolution in the data value chain, and synthesizes trends toward unified representations and foundation models.
DP-FLogTinyLLM combines federated learning, differential privacy, and LoRA-tuned tiny LLMs to match centralized log anomaly detection performance on Thunderbird and BGL datasets while preserving privacy.
citing papers explorer
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Conditional anomaly detection with soft harmonic functions
A soft harmonic function approach estimates label confidence for conditional anomaly detection while regularizing against isolated examples and distribution boundaries.
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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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A Catalog of Data Errors
A new catalog classifying 35 data error types into missing, incorrect, and redundant categories for tabular data, with definitions and examples to improve data quality management.
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Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection
An automated Python simulator, calibrated to one experimental run, generates consistent time-series data for many batch distillation scenarios including anomalies, forming an openly released hybrid dataset for deep anomaly detection.
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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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Feature Extraction in the Remote Sensing Data Value Chain: A Systematic Review of Methods and Applications
A systematic review that introduces a framework for feature extraction in remote sensing, traces its evolution in the data value chain, and synthesizes trends toward unified representations and foundation models.
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DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs
DP-FLogTinyLLM combines federated learning, differential privacy, and LoRA-tuned tiny LLMs to match centralized log anomaly detection performance on Thunderbird and BGL datasets while preserving privacy.