PAGER detects failures in deep regression models using a new framework.
problem Detecting failures in deep regression models.
method PAGER uses a combination of epistemic uncertainty and manifold non-conformity scores.
result PAGER accurately characterizes and detects failures in deep regressors.
Method distinguishes between failures and domain shifts in industrial data streams.
problem Confusing domain shifts with failures in industrial data.
method Modified Page-Hinkley changepoint detector and supervised domain-adaptation-based anomaly detection.
result Allows differentiation between failures and domain shifts.
Adaptive Stress Testing detects financial fraud by simulating potential failures.
problem Detecting and mitigating vulnerabilities in financial systems.
method Developed a simplified model using historical data and reinforcement learning.
result Identified the most likely path to system failure and improved fraud detection.
This work uses SVM to identify track component failures in AC Track Circuits.
problem Detecting and identifying specific track component failures in AC Track Circuits.
method Applied SVM classifier to STDS track circuit data.
result Successfully classified 15 different track component failures.
Deep neural networks predict CVCM track circuit failures early.
problem Subtle anomalies in CVCM track circuits lead to failures, causing disruptions.
method Deep neural networks classify anomalies before they escalate.
result Deep neural networks achieve 99.31% overall accuracy in detecting CVCM failures.
Study uses satellite data to predict tailings dam collapse risk.
problem Detecting early signs of tailings dam instability.
method Spectral analysis of satellite InSAR displacement time series data.
result Algorithm detects risk milestones up to 5 months before dam collapse.
This work improves safety validation of autonomous vehicles by finding interpretable failures.
problem Finding interpretable failures of autonomous systems in simulation.
method Signal temporal logic expressions optimized for high likelihood and human interpretability.
result Our methodology finds more interpretable failures with higher likelihood compared to baseline approaches.
Risk Advisor predicts and mitigates ML deployment failures.
problem Predicting and mitigating test-time failure risks of ML systems.
method Post-hoc meta-learner for estimating failure risks and uncertainties.
result Reliably predicts deployment-time failure risks across various ML models.
The paper tackles model failure detection and refitting in real-world systems.
problem Real-world data often fails statistical models due to heterogeneity.
method Develops tools for detecting and identifying model failures and refitting to improve accuracy.
result Empirical and theoretical results show the effectiveness of the proposed methodology.
Diverging Flows detects extrapolations in flow models, ensuring reliable predictions.
problem Flow models extrapolate into invalid data, leading to silent failures.
method Structurally enforce inefficient transport for off-manifold inputs.
result Effective detection of extrapolations without compromising predictive fidelity or inference latency.
In this work, we study the use of logistic regression in manufacturing failures detection. As a data set for the analysis, we used the data from Kaggle competition Bosch Production Line Performance. We considered the use of machine learning, linear and Bayesian models. For machine learning approach, we analyzed XGBoost…
This study surveys methods for detecting outliers in spatial data.
problem Detecting outliers in spatial data to avoid misinterpretation and enhance analysis.
method Survey of existing outlier detection methods for spatial data.
result Outliers in spatial data can be valuable if analyzed separately.
Study models systemic risks in BRICS banks under geopolitical shocks.
problem Systemic risks in BRICS banks under geopolitical shocks.
method Dynamic Time Warping, Temporal Graph Neural Network, Agent-Based Model.
result Geopolitical shocks cause more systemic damage than bank failures.
GE finds failures in autonomous systems without domain heuristics.
problem Finding failures in autonomous systems without domain-specific heuristics.
method Adaptive stress testing using go-explore (GE) algorithm.
result GE finds failures in scenarios other RL techniques cannot solve.
Enhances systemic risk analysis by incorporating debt valuation factors.
problem Systemic risk in financial networks due to bank failures.
method Incorporates debt valuation factors into existing risk analysis frameworks.
result Additional debt valuation factors substantially influence risk assessment outcomes.
Link invariants fail to detect most links with high probability.
problem Detecting specific link types using invariants.
method Mathematical proof and big-data analysis.
result Link invariants have a zero probability of detecting alternating links.
Method uses Seq2Seq learning to automatically generate recovery commands for ICT systems.
problem Manual decision-making for recovery commands is time-consuming and error-prone.
method Seq2Seq neural network model trained on past logs and commands.
result The model can estimate accurate recovery commands from new failures.
Unstructured data refers to information that does not have a predefined data model or is not organized in a pre-defined manner. Loosely speaking, unstructured data refers to text data that is generated by humans. In after-sales service businesses, there are two main sources of unstructured data: customer complaints, wh…
In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be present in the training set. The goal is to minimize the generalization error of the classifier on non-corrupted measurements while controlling th…
New method finds failures in high-fidelity simulators with fewer steps.
problem Finding failures in high-fidelity simulators is expensive and impractical.
method Adaptive stress testing with backward algorithm adaptation from low-fidelity to high-fidelity.
result Significantly fewer high-fidelity simulation steps needed to find failures.
New method uses KL divergence to detect out-of-distribution data effectively.
problem Flow-based models assign higher likelihoods to OOD data than ID data, making OOD detection challenging.
method Proposes a method leveraging KL divergence and local pixel dependence of representations for anomaly detection.
result Demonstrates effectiveness and robustness on prevalent benchmarks.
Machine learning predicts failure in brittle materials with high accuracy.
problem Predicting failure in brittle materials under repetitive loads.
method Phase-field model combined with supervised machine learning.
result Framework predicts failure with acceptable accuracy even in noisy data.
This research identifies flaws in drift detection methods and creates adversarial data streams to exploit them.
problem The challenge of detecting data distribution changes (drift) in real-time systems.
method Developed adversarial data streams to show weaknesses in existing drift detection schemes.
result Demonstrated that common drift detection methods can be fooled by adversarial data streams.
RAD detects anomalies in unreliable data streams with up to 98% accuracy.
problem Anomaly detection in unreliable data sources.
method Two-layer on-line learning framework with data cleansing and oracle learning.
result RAD improves anomaly detection accuracy by up to 28%.
Detecting early signs of failures (anomalies) in complex systems is one of the main goal of preventive maintenance. It allows in particular to avoid actual failures by (re)scheduling maintenance operations in a way that optimizes maintenance costs. Aircraft engine health monitoring is one representative example of a fi…
Machine learning models fail due to concept and data drift during pandemic.
problem Machine learning models trained before the pandemic are unreliable during the pandemic.
method Detect and diagnose concept and data drift in models.
result Model resilience and robustness are crucial for future predictions.
Studies in recent years have demonstrated that neural organization and structure impact an individual's ability to perform a given task. Specifically, individuals with greater neural efficiency have been shown to outperform those with less organized functional structure. In this work, we compare the predictive ability …
Study detects anomalies in robot vision data to predict hazards.
problem Detecting unexpected hazards in robot exploration data.
method Anomaly detection using autoencoders at different scales.
result Autoencoders improve anomaly detection performance on diverse robot scenarios.
In financial field, a robust software system is of vital importance to ensure the smooth operation of financial transactions. However, many financial corporations still depend on operators to identify and eliminate the system failures when financial software systems break down. This traditional operation method is time…
We investigate nearest neighbor and generative models for transferring pose between persons. We take in a video of one person performing a sequence of actions and attempt to generate a video of another person performing the same actions. Our generative model (pix2pix) outperforms k-NN at both generating corresponding f…
We study sequential change-point detection procedures based on linear sketches of high-dimensional signal vectors using generalized likelihood ratio (GLR) statistics. The GLR statistics allow for an unknown post-change mean that represents an anomaly or novelty. We consider both fixed and time-varying projections, deri…
New method combines FMEA and Bayesian Network for root cause analysis in lithium-ion battery production.
problem Complex cause-effect relationships in lithium-ion battery production.
method Combining FMEA with Bayesian Network to detect and resolve inconsistencies.
result Holistic method builds large-scale cross-process Bayesian Failure Network for root cause analysis.
In recent years, there have been many practical applications of anomaly detection such as in predictive maintenance, detection of credit fraud, network intrusion, and system failure. The goal of anomaly detection is to identify in the test data anomalous behaviors that are either rare or unseen in the training data. Th…
Federated learning enables training collaborative machine learning models at scale with many participants whilst preserving the privacy of their datasets. Standard federated learning techniques are vulnerable to Byzantine failures, biased local datasets, and poisoning attacks. In this paper we introduce Adaptive Federa…
This paper proposes and studies a detection technique for adversarial scenarios (dubbed deterministic detection). This technique provides an alternative detection methodology in case the usual stochastic methods are not applicable: this can be because the studied phenomenon does not follow a stochastic sampling scheme,…
Deep learning autoencoder detects bee colony anomalies.
problem Early detection of bee swarms and other unusual behaviors.
method Deep Recurrent Autoencoder model trained on sensor data.
result Autoencoder detects anomalies independent of their origin.
Paper proposes a framework to detect distribution shifts using embedding space geometry.
problem Detecting distribution shifts in candidate datasets to improve model generalizability.
method Non-parametric framework using embedding space geometry for two tests: robustness boundary and in-distribution/out-of-distribution classification.
result Both tests successfully detect distribution shifts in various scenarios for both synthetic and real-world datasets.
Cardiotoxicity related to cancer therapies has become a serious issue, diminishing cancer treatment outcomes and quality of life. Early detection of cancer patients at risk for cardiotoxicity before cardiotoxic treatments and providing preventive measures are potential solutions to improve cancer patients's quality of …
Proposes BA method for unbiased time series anomaly detection evaluation.
problem Anomalies in time series data are rare, making F1-score unreliable.
method Introduces Balanced Point Adjustment (BA) to address F1-score bias.
result BA provides fairer evaluation of time series anomaly detectors.
TSML tackles anomaly detection and pattern discovery in industrial time series data.
problem Extracting and exploiting information from large industrial data to reduce downtimes and manufacturing errors.
method TSML uses a pipeline of lightweight filters to process industrial time series data in parallel.
result TSML effectively detects anomalies and discovers patterns in industrial time series data.
Survey on LSTM-based anomaly detection for technical systems.
problem Detect anomalies in technical systems due to complex dynamics.
method Use LSTM networks and other AI techniques to detect anomalies considering temporal and contextual characteristics.
result Demonstrates the potential of LSTM networks and graph-based approaches for anomaly detection.
Novel method improves load estimation in power grids using anomaly and change point detection.
problem Improving load estimation in power grid systems.
method Combining unsupervised anomaly and change point detection methods for automatic filtering.
result Automatic load estimation is accurate with 90% estimates within a 10% error margin.
During the development of autonomous systems such as driverless cars, it is important to characterize the scenarios that are most likely to result in failure. Adaptive Stress Testing (AST) provides a way to search for the most-likely failure scenario as a Markov decision process (MDP). Our previous work used a deep rei…
New objective function improves model robustness.
problem Machine Learning robustness issues.
method Conditional Entropy Bottleneck (CEB) objective function.
result CEB models improve robust generalization.
New method detects anomalies in systems influenced by their environment.
problem Detecting anomalies in systems under environmental influence.
method Adversarial learning and time series representation learning.
result Successfully addresses label sparsity and subjectivity in anomaly detection.
Measuring uncertainty is a promising technique for detecting adversarial examples, crafted inputs on which the model predicts an incorrect class with high confidence. But many measures of uncertainty exist, including predictive en- tropy and mutual information, each capturing different types of uncertainty. We study th…
C-PP-COAD detects anomalies with limited real data, reducing dependency on real calibration data.
problem Limited real calibration data for online anomaly detection.
method Context-aware prediction-powered conformal online anomaly detection (C-PP-COAD).
result Significantly reduces dependency on real calibration data without compromising FDR control.
Wind power, as an alternative to burning fossil fuels, is abundant and inexhaustible. To fully utilize wind power, wind farms are usually located in areas of high altitude and facing serious ice conditions, which can lead to serious consequences. Quick detection of blade ice accretion is crucial for the maintenance of …