arXiv Machine Learning

Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems

arXiv:2607. 06094v1 Announce Type: new Abstract: Faults on a cyber-physical system (CPS) are too rare and unrepresentative to characterise, or even to select a model on, so detection must instead model normal behaviour; the standard point-adjusted evaluation, however, rewards detectors that never do.

Hugging Face Trending Papers

Jul 7

Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems

Faults on a cyber-physical system (CPS) are too rare and unrepresentative to characterise, or even to select a model on, so detection must instead model normal behaviour; the standard point-adjusted evaluation, however, rewards detectors that never do. CPS normal behaviour is the union of many imbalanced, curved, thin-fringed operating regimes rather than a single blob; we state this structure as ten assumptions (A1-A10), abbreviated Massive, Implicit, Imbalanced Multimodality (MIIM).

multimodal

arXiv Machine Learning

Jul 21

Interpretable Anomaly and Drift Detection with Gaussian Mixture Models

arXiv:2607. 16811v1 Announce Type: new Abstract: We revisit Gaussian Mixture Models (GMMs) as a lightweight, interpretable tool for anomaly detection and, in particular, for detecting distributional drift in data streams.

By Behnam Asadi

benchmarks

arXiv Machine Learning

Aug 14

Dimension-Calibrated Unexplained Mass: An Interpretable Drift Statistic for Contamination Monitoring in Data Streams

arXiv:2607. 16811v4 Announce Type: replace Abstract: Drift detectors that work tend not to explain themselves, and drift detectors that explain themselves tend to fail in high dimension.

By Behnam Asadi

benchmarks

arXiv Machine Learning

Aug 4

Dimension-Calibrated Unexplained Mass: An Interpretable GMM Drift Statistic that Matches Kernel Two-Sample Tests

arXiv:2607. 16811v3 Announce Type: replace Abstract: Drift detectors that work tend not to explain themselves, and drift detectors that explain themselves tend to fail in high dimension.

By Behnam Asadi

benchmarks

arXiv Machine Learning

Jun 15

Conformal calibration and look-elsewhere effect in anomaly detection for new-physics searches

arXiv:2606. 13780v1 Announce Type: cross Abstract: Machine-learned anomaly detection is reshaping searches for new physics, but it has outrun the statistics used to interpret it.

By Jack Y. Araz, Michael Spannowsky

arXiv Machine Learning

Aug 31

Localizing Global Discrepancies: Marginal Contributions and Contextual Anomaly Detection

arXiv:2608.28375v1 Announce Type: cross Abstract: Global goodness-of-fit and discrepancy statistics can establish that a sample departs from a reference distribution without identifying which observa...

By Tommaso dorigo

benchmarks

arXiv AI

Aug 18

Cross-Domain Industrial Fault Detection by Causal Mechanism Monitoring

arXiv:2608. 14666v1 Announce Type: new Abstract: Unsupervised fault detection in industrial systems is dominated by reconstruction based methods that monitor individual sensor marginal distributions.

By Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley, Sunil Aryal

rag multimodal safety

arXiv Machine Learning

Aug 6

Canonical Joint Energy-Based Model on CIFAR-10: failure modes and practical indistinguishability of Predictor-Corrector and SGLD samplers

arXiv:2608. 05025v1 Announce Type: new Abstract: Joint Energy-Based Models (JEM) unify classification and generation within a single network and support out-of-distribution (OOD) detection.

By Dmytro Knopov

Hugging Face Trending Papers

Sep 21

Exposing Blind Spots in Deep Imbalanced Regression Evaluation

Deep Imbalanced Regression (DIR) addresses a common failure mode of regression models: target distributions are highly non-uniform, causing models to perform best in densely populated target regions e...

multimodal benchmarks

arXiv Machine Learning

Sep 23

Exposing Blind Spots in Deep Imbalanced Regression Evaluation

arXiv:2609.25152v1 Announce Type: new Abstract: Deep Imbalanced Regression (DIR) addresses a common failure mode of regression models: target distributions are highly non-uniform, causing models to p...

By Noah C. Puetz, Jens U. Brandt, Marc Hilbert, Elena Raponi, Thomas B\"ack, Thomas Bartz-Beielstein

multimodal benchmarks

arXiv Statistics ML

Sep 2

Deep Skew-t Mixture Models

arXiv:2609.00773v1 Announce Type: cross Abstract: High-dimensional clustering is challenging when component distributions are both heavy-tailed and directionally asymmetric. We propose a deep skew-$t...

By Jinran Wu, You-Gan Wang, Geoffrey J. McLachlan

benchmarks

arXiv Machine Learning

Jun 11

Online Shift Detection and Conformal Adaptation for Deployed Safety Classifiers

arXiv:2606. 11949v1 Announce Type: new Abstract: We present an online monitoring system for distributional shift in deployed safety classifiers, using calibrated sequential statistics to detect when a classifier has moved out of distribution.

By Jun Wen Leong

llms rag safety

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