A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
PAGER detects failures in deep regression models using a new framework.
Method distinguishes between failures and domain shifts in industrial data streams.
Adaptive Stress Testing detects financial fraud by simulating potential failures.
This work uses SVM to identify track component failures in AC Track Circuits.
Deep neural networks predict CVCM track circuit failures early.
Study uses satellite data to predict tailings dam collapse risk.
This work improves safety validation of autonomous vehicles by finding interpretable failures.
Risk Advisor predicts and mitigates ML deployment failures.
The paper tackles model failure detection and refitting in real-world systems.
Diverging Flows detects extrapolations in flow models, ensuring reliable predictions.
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.
Study models systemic risks in BRICS banks under geopolitical shocks.
GE finds failures in autonomous systems without domain heuristics.
Enhances systemic risk analysis by incorporating debt valuation factors.
Link invariants fail to detect most links with high probability.
Method uses Seq2Seq learning to automatically generate recovery commands for ICT systems.
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.
Machine learning predicts failure in brittle materials with high accuracy.
This research identifies flaws in drift detection methods and creates adversarial data streams to exploit them.
Recent research has revealed that deep generative models including flow-based models and Variational Autoencoders may assign higher likelihoods to out-of-distribution (OOD) data than in-distribution (ID) data. However, we cannot sample OOD data from the model. This counterintuitive phenomenon has not been satisfactoril…
Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i.e., features and labels are correctly set. However, data collected from the wild can be unreliable due to careless annotations o…
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.
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.
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.
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.
Much of the field of Machine Learning exhibits a prominent set of failure modes, including vulnerability to adversarial examples, poor out-of-distribution (OoD) detection, miscalibration, and willingness to memorize random labelings of datasets. We characterize these as failures of robust generalization, which extends …
Paper proposes a framework to detect distribution shifts using embedding space geometry.
Proposes BA method for unbiased time series anomaly detection evaluation.
TSML tackles anomaly detection and pattern discovery in industrial time series data.
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 …
Survey on LSTM-based anomaly detection for technical systems.
Novel method improves load estimation in power grids using anomaly and change point detection.
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 method detects anomalies in systems influenced by their environment.
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.
During the past decade, many anomaly detection approaches have been introduced in different fields such as network monitoring, fraud detection, and intrusion detection. However, they require understanding of data pattern and often need a long off-line period to build a model or network for the target data. Providing re…