Wojciech Kamiński disproved a spiral claim for conformal geodesics.
problem Proving that conformal geodesics cannot spiral.
method Provided a counterexample and illustrated proof failure.
result Lemma 4.6 proof was flawed.
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48 results for “proof failure”
We identify and study two common failure modes for early training in deep ReLU nets. For each we give a rigorous proof of when it occurs and how to avoid it, for fully connected and residual architectures. The first failure mode, exploding/vanishing mean activation length, can be avoided by initializing weights from a …
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.
This work uses image generation models to find vision model bugs.
problem Automatically discovering failures in vision models.
method Conditional text-to-image generation and captioning models.
result Demonstrated utility of large-scale generative models to find vision model bugs.
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.
Rational expectations as a tool for predicting failure of weighted k-out-of-n reliability systemsphysics.soc-ph
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.
Bayesian Joint Model of Multi-Sensor and Failure Event Data for Multi-Mode Failure Predictionstat.ME
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.
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.
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.
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.
Prognostics and Health Management (PHM) is an emerging engineering discipline which is concerned with the analysis and prediction of equipment health and performance. One of the key challenges in PHM is to accurately predict impending failures in the equipment. In recent years, solutions for failure prediction have evo…
As the success of deep learning reaches more grounds, one would like to also envision the potential limits of deep learning. This paper gives a first set of results proving that certain deep learning algorithms fail at learning certain efficiently learnable functions. The results put forward a notion of cross-predictab…
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.
Prognostics for Autonomous Deep-Space Habitat Health Management under Multiple Unknown Failure Modesstat.ML
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.
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.
We aim to predict and explain service failures in supply-chain networks, more precisely among last-mile pickup and delivery services to customers. We analyze a dataset of 500,000 services using (1) supervised classification with Random Forests, and (2) Association Rules. Our classifier reaches an average sensitivity of…
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.
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.
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.
This paper addresses the problem of evaluating learning systems in safety critical domains such as autonomous driving, where failures can have catastrophic consequences. We focus on two problems: searching for scenarios when learned agents fail and assessing their probability of failure. The standard method for agent e…
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%.
The loss of a few neurons in a brain rarely results in any visible loss of function. However, the insight into what "few" means in this context is unclear. How many random neuron failures will it take to lead to a visible loss of function? In this paper, we address the fundamental question of the impact of the crash of…
In the last two years, more than 200 papers have been written on how machine learning (ML) systems can fail because of adversarial attacks on the algorithms and data; this number balloons if we were to incorporate papers covering non-adversarial failure modes. The spate of papers has made it difficult for ML practition…
Two BO methods improve reliability optimization for rare failures.
problem Maximizing reliability of designs subject to random perturbations.
method Bayesian optimization with Thompson sampling and knowledge gradient.
result Proposed methods outperform existing techniques in extreme failure probability scenarios.
Entropy-based GP adaptive design improves failure probability estimation.
problem Limited accuracy in failure probability estimation due to model evaluation costs.
method Entropy-based Gaussian process (GP) adaptive design combined with multifidelity importance sampling (MFIS).
result More accurate failure probability estimates and higher confidence.
Adaptive Synaptic Failure Enables Sampling from Posterior Predictive Distributions in the Brainq-bio.NC
Brain uses synaptic failure to sample from posterior distributions.
problem Bayesian inference in the brain's probabilistic computations.
method Adapting synaptic failure to sample posterior predictive distributions.
result Synaptic failure enables sampling of complete posterior predictive distributions.
Study constructs balanced datasets for seismic failure prediction.
problem Imbalanced datasets limit machine learning performance in seismic failure prediction.
method Framework with three steps: GMF identification, probability density estimation, and sample transformation.
result Framework improves machine learning performance in seismic failure mode 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.
The purpose of this article is to \begin{enumerate} \item define the -fold center of mass arrangement for points in the plane, \item give elementary properties of and \item give consequences concerning the space of distinct points in the plane, no four of which are the vertices of …
Improves FI-PINNs by combining re-sampling and subset simulation for better failure probability estimation.
problem Estimating failure probability in physics-informed neural networks (PINNs).
method Adaptive sampling with re-sampling and subset simulation, using cosine-annealing for uniform to adaptive transition.
result Significant improvement in estimating failure probability and generating new training points in the failure region.
A new algorithm learns from failures to optimize under constraints efficiently.
problem Optimizing under unknown constraints with limited failure tolerance.
method Excursion search controls risk as a function of a failure budget.
result The algorithm achieves lower regret and uses failures budget more efficiently.
Determining possible failure scenarios is a critical step in the evaluation of autonomous vehicle systems. Real-world vehicle testing is commonly employed for autonomous vehicle validation, but the costs and time requirements are high. Consequently, simulation-driven methods such as Adaptive Stress Testing (AST) have b…
Study shows certain infinite-ended groups are not quasi-isometrically rigid.
problem Understanding when infinite-ended groups are quasi-isometrically rigid.
method Combining results on subgroups and hyperbolic groups, adapting Whyte's argument.
result Proves certain infinite-ended groups are not quasi-isometrically rigid.
Scalable Autonomous Vehicle Safety Validation through Dynamic Programming and Scene Decompositioncs.RO
New approach uses dynamic programming to efficiently discover failures in autonomous vehicle simulations.
problem Efficiently discovering rare failure events in autonomous vehicle simulations.
method Approximate dynamic programming and scene decomposition to estimate failure distribution.
result Increased number of failures discovered compared to baseline approaches.
Adaptive PINNs improve accuracy by adding points where solutions are uncertain.
problem Inadequate sampling in PINNs leads to inaccurate solutions, especially near singularities.
method FI-PINNs use failure probability to dynamically add points, improving numerical accuracy.
result FI-PINNs achieve better accuracy through adaptive sampling, as proven by rigorous error bounds.
Bayesian inference models failure distributions in autonomous systems.
problem Estimating the distribution of failures in complex systems.
method Bayesian inference using system dynamics rollouts and gradient computation.
result Improves sample efficiency and parameter space coverage in autonomous systems.
We develop a new method to estimate failure probabilities in complex systems.
problem Estimating failure probabilities in safety-critical autonomous systems is challenging due to the rarity of failures and large state spaces.
method We propose an adaptive importance sampling algorithm that minimizes forward Kullback-Leibler divergence and uses Markov score ascent methods.
result Our method provides more accurate failure probability estimates than existing techniques.
Data-Driven Failure Prediction in Brittle Materials: A Phase-Field Based Machine Learning Frameworkcs.CE
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.
Study predicts P2P lending platform failures using machine learning.
problem Predicting failures of P2P lending platforms in China.
method Used machine learning models with filter and wrapper methods, forward selection, and backward elimination.
result Identified robust variables for predicting platform failures with high AUC and F1 scores.
To provide proactive fault tolerance for modern cloud data centers, extensive studies have proposed machine learning (ML) approaches to predict imminent disk failures for early remedy and evaluated their approaches directly on public datasets (e.g., Backblaze SMART logs). However, in real-world production environments,…
This study uses TDA to map corporate failure, revealing distinct regions of risk.
problem Understanding and predicting corporate default risk.
method Topological Data Analysis (TDA) applied to Altman's Z-score model.
result Firms do not cluster neatly along default predictors, suggesting complex risk landscapes.
In this paper, we consider a problem of failure prediction in the context of predictive maintenance applications. We present a new approach for rare failures prediction, based on a general methodology, which takes into account peculiar properties of technical systems. We illustrate the applicability of the method on th…
Locally learned synaptic failure enables complete Bayesian inference.
problem Bayesian inference in neural networks.
method Biologically constrained neural network with synaptic failure and lateral inhibition.
result Synaptic failure allows sampling from both epistemic and aleatoric distributions.
While machine learning systems show high success rate in many complex tasks, research shows they can also fail in very unexpected situations. Rise of machine learning products in safety-critical industries cause an increase in attention in evaluating model robustness and estimating failure probability in machine learni…
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.
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.