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.
Predicting MRI coil failures using time series classification.
problem Early detection of MRI hardware failures to prevent downtime.
method Training a statistical model on sequential image data features over time.
result LSTMs achieved an F-score of 86.43% and 98.33% accuracy in predicting coil damage.
GAN-FP uses GANs to predict equipment failures from imbalanced data.
problem Accurately predicting equipment failures with limited data and high imbalance.
method GAN-FP employs two GAN networks to generate and classify imbalanced data, optimizing a weighted loss objective and a consistency GAN.
result GAN-FP outperforms traditional methods in imbalanced failure prediction.
Proposes a deep neural network for early disk drive failure prediction.
problem Early prediction of disk drive failure using multivariate time series sensor data.
method Enriched features derived from sensor data through transformations, combined with ensemble learning and deep neural network architecture.
result Significantly improved classification accuracy in predicting disk drive failure.
Proposes a method to classify with sensor failures, improving accuracy and anomaly detection.
problem Learning robust binary classifier with possible sensor failures.
method Geometric-Entropy-Minimization regularized Maximum Entropy Discrimination (GEM-MED) method.
result Improved performance in classification accuracy and anomaly detection rate.
Predict and explain service failures in supply-chain networks using data models.
problem Predict and explain service failures in supply-chain networks, particularly last-mile pickup and delivery.
method Used supervised classification with Random Forests and Association Rules on a dataset of 500,000 services.
result Classifier reaches an average sensitivity of 0.7 and specificity of 0.7 for 5 types of failure.
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.
Predicting MRI coil failures using image features and ensemble learning.
problem Ensuring trouble-free operation of MRI systems by predicting hardware failures.
method Two-level data analysis with neural networks and Random Forest ensemble learning.
result Improved prediction results with an F-score of 94.14% and an accuracy of 99.09%.
Active Kriging Monte Carlo simulation method with conformal certification for failure probability estimation
problem Failure probability estimation in structural reliability analysis
method Active learning framework with conformal prediction
result Improved uncertainty quantification and reliability of failure probability estimates
Proposes a new confidence criterion for deep neural networks to predict failures.
problem Predicting failures in deep neural networks.
method Introduces True Class Probability (TCP) as a new confidence criterion and proposes a learning scheme to estimate it.
result The proposed approach consistently outperforms existing methods in failure prediction.
Study uses machine learning, linear, and Bayesian models for logistic regression in manufacturing failures detection.
problem Manufacturing failures detection using logistic regression models.
method Machine learning (XGBoost), linear, and Bayesian approaches for logistic regression.
result Bayesian approach provides statistical distribution for model parameters, useful for probabilistic analysis.
ProEval efficiently estimates AI performance and discovers failures using pre-trained Gaussian Processes.
problem Resource-intensive evaluation of generative AI models.
method ProEval uses pre-trained Gaussian Processes and Bayesian quadrature to estimate performance and discover failures.
result ProEval requires significantly fewer samples to achieve accurate performance estimates and reveals more diverse failure cases.
New method predicts RUL and failure modes from partial data.
problem Predicting RUL and failure modes from incomplete data.
method Formulated as vector General Value Function (GVF) prediction on an absorbing degradation process, using TD(n,λ) for estimation. result TD improves RUL and failure-mode prediction compared to Monte Carlo methods, especially under scarce complete labels.
Study identifies failure modes of machine learning models in out-of-distribution settings.
problem Machine learning models fail to generalize well to new, unseen data.
method Theoretical study of gradient-descent-trained linear classifiers on easy-to-learn tasks, followed by experiments on modern neural networks.
result Two failure modes of spurious correlations are uncovered: geometric and statistical.
A new machine learning method calculates failure probability efficiently and accurately.
problem Computing the probability of failure for complex systems.
method Penalized Profile Support Vector Machine with adaptive sampling and clustering.
result The method minimizes model evaluations while preserving decision boundary geometry.
A new machine learning framework predicts financial IT system failures.
problem Traditional manual system failure detection is inefficient and time-consuming.
method Hybrid machine learning framework with data cleaning, anomaly identification, and severity classification modules.
result The proposed method outperforms traditional methods on real-world financial data.
Interpretable survival analysis improves heart failure risk prediction.
problem Improving heart failure risk prediction using survival analysis.
method Survival stacking, ControlBurn, Explainable Boosting Machines.
result Achieves state-of-the-art performance and provides novel insights.
Proposes a method to robustly classify and detect anomalies.
problem Learning robust binary classifiers in the presence of corrupted measurements.
method Geometric-Entropy-Minimization regularized Maximum Entropy Discrimination (GEM-MED) method.
result Improved performance in classification accuracy and anomaly detection rate.
New approach combines invariance and information bottleneck for OOD generalization.
problem OOD generalization failures in classification tasks.
method Revisit linear regression tasks, prove information bottleneck constraint necessary, propose combined approach.
result Combined invariance and information bottleneck approach improves OOD generalization.
Predicts path failures in evolving networks using deep learning.
problem Predicting path failures in time-evolving graphs.
method LRGCN, SAPE
result LRGCN outperforms other methods in path failure prediction.
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.
G-SHAP generates multiple types of explanations for machine learning models.
problem Understanding model predictions and their differences across groups.
method Generalization of SHAP method to produce additional types of explanations.
result G-SHAP produces explanations for classification, intergroup differences, and model failure.
This work aims to reduce inexplicable errors in deep neural networks by obtaining class-level semantics and penalizing misclassifications.
problem Deep neural networks misclassify images, leading to inexplicable errors that can harm trust and societal impact.
method Obtain class-level semantics, propose Weighted Loss Functions (WLFs), and train classifiers with these methods.
result Trained networks have more explicable failure modes and comparable accuracy to existing methods.
S. Satoh has defined a construction to obtain a ribbon torus knot given a welded knot. This construction is known to be surjective. We show that it is not injective. Using the invariant of the peripheral structure, it is possible to provide a restriction on this failure of injectivity. In particular we also provide an …
Alternative method improves SVM for data classification.
problem SVM's weaknesses in time processing, optimization, multi-class generalization, and unbalanced classes.
method Minimum distance to optimal subspaces containing mapped classes.
result Improved performance with fewer shortcomings.
MD tree diagnoses model failures using loss landscape metrics.
problem Diagnose model failures without knowing training configuration.
method MD tree based on loss landscape metrics.
result MD tree achieves 87.7% accuracy in dataset transfer tasks, outperforming validation-based approaches.
Bayes-TrEx finds in-distribution examples for model inspection.
problem Challenges in interpreting neural networks, especially high-confidence failures and ambiguous classifications.
method Bayesian sampling approach to find in-distribution examples with specified prediction confidence.
result Bayes-TrEx enables more flexible holistic model analysis than just inspecting the test set.
Economics tool predicts failure times in reliability systems.
problem Predicting optimal failure times in weighted k-out-of-n reliability systems with heterogeneous component failure.
method Using rational expectations to analyze and predict failure times in reliability systems with heterogeneous component failure.
result Different measures are optimal for predicting system failure depending on component failure distributions.
Simplified non-contrastive learning avoids representation collapse.
problem Training failure modes in self-supervised learning.
method Hyperdimensional computing and inductive bias.
result The approach avoids representation collapses.
Unified model predicts multi-mode failure with multi-sensor data.
problem Independent failure mode and RUL prediction ignores inherent relationship.
method Hierarchical Bayesian framework with Cox model, Gaussian process, and multinomial distributions.
result Robust uncertainty quantification and accurate prediction of multi-mode failure.
The paper investigates causal relationships in heart failure prediction using machine learning.
problem Understanding the causal relationships between clinical variables and heart failure.
method Proposes a new computational framework for causal structure discovery (CSD) of mixed-type clinical variables for binary disease outcomes.
result Feature importance from nonlinear classifiers strongly correlates with causal strength of variables, but not differentiating cause and effect.
Framework classifies machine learning failures into intentional and unintentional.
problem Understanding and preventing failures in machine learning systems.
method Developed a taxonomy of machine learning failure modes.
result Stakeholders found the framework useful for discussing machine learning failures.
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.
CalNF models rare failures with limited data, improving safety in autonomous systems.
problem Challenges in modeling and debugging rare safety-critical failures due to limited data.
method CalNF, a self-regularized framework for posterior learning from limited data.
result Achieves state-of-the-art performance on data-limited failure modeling and inverse problems.
Framework predicts remaining useful life of DSH subsystems under unknown failure modes.
problem Predicting remaining useful life of DSH subsystems with unknown failure modes.
method Unsupervised framework using mixture of Gaussian regressions and Expectation-Maximization algorithm.
result Improved prediction accuracy and interpretability of RUL.
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.
New method predicts rare failures in aerospace systems.
problem Rare failure prediction in aerospace applications.
method Event matching based on technical system peculiarities.
result Illustrated the method's applicability on aircraft operations.
DC-Prophet predicts catastrophic server failures in datacenters.
problem Forecasting catastrophic machine failures in datacenters.
method Two-stage framework based on One-Class Support Vector Machine and Random Forest.
result DC-Prophet achieves an AUC of 0.93 and F3-score of 0.88 in predicting the next machine failure.
Adversarial method finds rare catastrophic failures in safety-critical agents.
problem Evaluating safety-critical learning systems for catastrophic failures.
method Adversarial evaluation approach focusing on rare adversarial situations.
result Adversarial evaluation finds catastrophic failures and estimates failure rates faster.
The paper calculates the likelihood of a financial market failure involving multiple major banks.
problem Estimating the probability of a market failure involving multiple globally important banks.
method Multivariate Cox process across G-SIBs, deriving various theorems on market failure probabilities.
result The probability of a market failure increases with the number of G-SIBs and is inevitable if there are too many.
Paper proposes a method to predict disk failures using multi-layer domain adaptive learning.
problem Traditional machine learning models struggle to predict disk failures due to limited data.
method Multi-layer domain adaptive learning with source and target domains.
result The proposed method improves failure prediction accuracy on disk data with few failure samples.
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.
Improved AST method finds more useful failure scenarios for autonomous vehicles.
problem Finding useful failure scenarios for autonomous vehicle validation is challenging.
method Adaptive Stress Testing with reward augmentation, modified to encode domain information.
result The modified AST method discovers a larger and more expressive subset of failure scenarios.
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.
System predicts respiratory failure up to 8 hours early.
problem Early detection of respiratory failure in ICU patients.
method Machine learning on ICU patient monitoring data.
result System outperforms traditional clinical decision-making.
Predicts failure of autonomous vehicle steering control models.
problem Evaluating and predicting failure of machine learning models in safety-critical applications.
method Trains a student model to predict the main model's error based on saliency maps.
result Preliminary results show the failure predictor model works on autonomous vehicle steering control systems.
New method avoids failures in physics-constrained systems using active learning.
problem Handling fatal failures in systems governed by physics constraints.
method Develops a novel active learning method that considers implicit physics constraints.
result Achieves zero-failure in composite fuselage assembly process without explicit failure regions.
DeepSIP predicts network failures' impact using CNN from syslog and traffic data.
problem Predicting service impact from network failures.
method Temporal multimodal CNN for predicting time to recovery and traffic loss.
result DeepSIP reduced prediction error by approximately 50%.