Coupling-Grouped XY-QAOA enables joint anomaly-feature selection via a constraint-preserving grouped-angle QAOA variant, achieving 45.9-61.3% circuit depth reduction and larger feasible executions (64 qubits at p=2) on IBM Heron hardware compared to standard approaches.
hub
Anomaly detection: A survey
16 Pith papers cite this work. Polarity classification is still indexing.
16
Pith papers citing it
hub tools
citation-role summary
background 4
citation-polarity summary
verdicts
UNVERDICTED 16roles
background 3polarities
background 3representative citing papers
A review paper that organizes industrial visual sim-to-real literature into CAD-available, CAD-unavailable, and boundary-prior regimes based on the type of prior information available.
A soft harmonic function approach estimates label confidence for conditional anomaly detection while regularizing against isolated examples and distribution boundaries.
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.
Training in graph-regularized quantum networks increases spectral dimension by 0.23 and enables anomaly detection via Bloch drift (ROC-AUC ≥0.9) while bosonic enhancement correlates with Fiedler splits (r=-0.50).
Overlapping inference windows improve reconstruction-based time series anomaly detection by up to 28% relative gain across models on TSB-AD and UCR benchmarks and can alter rankings.
RASC decomposes ill-posed global calibration of drifting BJT 2D sensor arrays into local robust estimations reconciled by consensus on overlap graphs, cutting fixed-pattern residual 71% on real 16x16 data and matching centralized EKF in simulations with lower bandwidth.
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.
Ensemble anomaly detection framework for real-time risk calculation monitoring outperforms single methods with F1 scores of 61-79% on proprietary credit-derivatives data using injected anomalies.
Spatial Learning Entropy Maps derived from MLP weight adaptations during spatial pixel prediction tasks highlight image points with high learning impact.
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.
Presents a three-component fusion AI agent for multi-vector fraud and AML detection in retail/corporate banking using LSTM, statistical, and graph modules on synthetic data, reporting F1 scores of 0.787 (transactions) and 0.867 (sessions).
An AI agent for ACMIS uses supervised anomaly detection, behavioral analytics, and an NLP chatbot to report 0.966 macro F1 on simulated threat data, outperforming rule-based and LSTM baselines.
citing papers explorer
-
Coupling-Grouped XY-QAOA for Joint Anomaly-Feature Selection
Coupling-Grouped XY-QAOA enables joint anomaly-feature selection via a constraint-preserving grouped-angle QAOA variant, achieving 45.9-61.3% circuit depth reduction and larger feasible executions (64 qubits at p=2) on IBM Heron hardware compared to standard approaches.
-
Prior Availability in Industrial Visual Sim-to-Real: A Review of CAD-Guided and CAD-Unavailable Regimes
A review paper that organizes industrial visual sim-to-real literature into CAD-available, CAD-unavailable, and boundary-prior regimes based on the type of prior information available.
-
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.
-
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.
-
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.
-
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.
-
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.
-
Spectral Geometry and Bosonic-Bloch Probes: Explorations in Quantum Learning
Training in graph-regularized quantum networks increases spectral dimension by 0.23 and enables anomaly detection via Bloch drift (ROC-AUC ≥0.9) while bosonic enhancement correlates with Fiedler splits (r=-0.50).
-
Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection
Overlapping inference windows improve reconstruction-based time series anomaly detection by up to 28% relative gain across models on TSB-AD and UCR benchmarks and can alter rankings.
-
RASC: Region-Aware Self-Calibration for Dense 2D Sensor Arrays
RASC decomposes ill-posed global calibration of drifting BJT 2D sensor arrays into local robust estimations reconciled by consensus on overlap graphs, cutting fixed-pattern residual 71% on real 16x16 data and matching centralized EKF in simulations with lower bandwidth.
-
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.
-
How to spot outliers: an Ensemble Anomaly Detection Framework
Ensemble anomaly detection framework for real-time risk calculation monitoring outperforms single methods with F1 scores of 61-79% on proprietary credit-derivatives data using injected anomalies.
-
Learning Entropy and Spatial Adaptation Dynamics of Multilayer Perceptrons for Structural Point Extraction
Spatial Learning Entropy Maps derived from MLP weight adaptations during spatial pixel prediction tasks highlight image points with high learning impact.
-
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.
-
An AI Security Agent for Banking: Multi-Vector Fraud and AML Detection Across Retail and Corporate Accounts
Presents a three-component fusion AI agent for multi-vector fraud and AML detection in retail/corporate banking using LSTM, statistical, and graph modules on synthetic data, reporting F1 scores of 0.787 (transactions) and 0.867 (sessions).
-
An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response
An AI agent for ACMIS uses supervised anomaly detection, behavioral analytics, and an NLP chatbot to report 0.966 macro F1 on simulated threat data, outperforming rule-based and LSTM baselines.