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.
IDK improves anomaly detection for points and groups without explicit learning.
We propose the Autoencoding Binary Classifiers (ABC), a novel supervised anomaly detector based on the Autoencoder (AE). There are two main approaches in anomaly detection: supervised and unsupervised. The supervised approach accurately detects the known anomalies included in training data, but it cannot detect the unk…
The challenge of efficiently identifying anomalies in data sequences is an important statistical problem that now arises in many applications. Whilst there has been substantial work aimed at making statistical analyses robust to outliers, or point anomalies, there has been much less work on detecting anomalous segments…
We consider the problem of detecting anomalies in a large dataset. We propose a framework called Partial Identification which captures the intuition that anomalies are easy to distinguish from the overwhelming majority of points by relatively few attribute values. Formalizing this intuition, we propose a geometric anom…
CAD detects anomalies and selects prototypes using polyhedron curvature.
Proposes BA method for unbiased time series anomaly detection evaluation.
Anomaly detection is referred to as a process in which the aim is to detect data points that follow a different pattern from the majority of data points. Anomaly detection methods suffer from several well-known challenges that hinder their performance such as high dimensionality. Autoencoders are unsupervised neural ne…
Outlier detection has received special attention in various fields, mainly for those dealing with machine learning and artificial intelligence. As strong outliers, anomalies are divided into the point, contextual and collective outliers. The most important challenges in outlier detection include the thin boundary betwe…
A new model classifies surface anomalies in 3D point cloud data.
We propose a novel non-parametric adaptive anomaly detection algorithm for high dimensional data based on rank-SVM. Data points are first ranked based on scores derived from nearest neighbor graphs on n-point nominal data. We then train a rank-SVM using this ranked data. A test-point is declared as an anomaly at alpha-…
In recent years, there has been a growing interest in identifying anomalous structure within multivariate data streams. We consider the problem of detecting collective anomalies, corresponding to intervals where one or more of the data streams behaves anomalously. We first develop a test for a single collective anomaly…
Enhances deep learning models for anomaly detection in time series data.
Novel method improves load estimation in power grids using anomaly and change point detection.
DTOR explains anomalies with rule-based explanations.
ReRe detects anomalies in real-time for time series data.
PIF detects anomalies in structured patterns using preference embedding.
Metric anomalies arising from a distribution of point defects (intrinsic interstitials, vacancies, point stacking faults), thermal deformation, biological growth, etc. are well known sources of material inhomogeneity and internal stress. By emphasizing the geometric nature of such anomalies we seek their representation…
The random cluster model is used to define an upper bound on a distance measure as a function of the number of data points to be classified and the expected value of the number of classes to form in a hybrid K-means and regression classification methodology, with the intent of detecting anomalies. Conditions are given …
We present five methods to the problem of network anomaly detection. These methods cover most of the common techniques in the anomaly detection field, including Statistical Hypothesis Tests (SHT), Support Vector Machines (SVM) and clustering analysis. We evaluate all methods in a simulated network that consists of nomi…
Graph-based methods for anomaly detection and semi-supervised learning.
We propose a non-parametric anomaly detection algorithm for high dimensional data. We first rank scores derived from nearest neighbor graphs on -point nominal training data. We then train limited complexity models to imitate these scores based on the max-margin learning-to-rank framework. A test-point is declared as…
This paper tackles the 'Clever Hans' effect in anomaly detection models.
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…
OneFlow detects anomalies by finding a minimal volume region, outperforming other methods.
Hashing detects anomalies in structured data efficiently.
TAnoGan detects anomalies in time series data using GANs.
A novel one-class classifier fusion method for robust anomaly detection.
A new framework detects anomalies in structured data.
Paper introduces an unsupervised tensor-based anomaly detection method for spatiotemporal data.
Study examines how discretization affects anomaly detection in datasets.
TPA-AD detects axle-box bearing anomalies using pseudo anomalies near normal boundaries.
TimeInf estimates data contribution in time series data, improving model performance and anomaly detection.
In this paper, we introduce Anomaly Contribution Explainer or ACE, a tool to explain security anomaly detection models in terms of the model features through a regression framework, and its variant, ACE-KL, which highlights the important anomaly contributors. ACE and ACE-KL provide insights in diagnosing which attribut…
RAID algorithm detects anomalies in real-time IoT systems.
Autoencoders misidentify anomalies due to data topology.
Paper proposes a new dataset for group anomaly detection in physics.
Anomaly flow studied on flat and non-flat nilmanifolds.
GANs improve anomaly detection in power plants, achieving nearly perfect classification.
In gauge theory, the Faddeev-Mickelsson-Shatashvili anomaly arises as a prolongation problem for the action of the gauge group on a bundle of projective Fock spaces. In this paper, we study this anomaly from the point of view of bundle gerbes and give several equivalent descriptions of the obstruction. These include li…
Paper proposes RAN for better anomaly detection in time series data.
Study -flows reducing to complex geometry flows, focusing on -anomaly and -Laplacian coflow.
OracleAD detects multivariate time series anomalies without labels.
DiFF-RF detects point-wise and collective anomalies using random partitioning trees.
STRIC detects anomalies in time series by analyzing residual signals.
Time series data is ubiquitous in the real-world problems across various domains including healthcare, social media, and crime surveillance. Detecting anomalies, or irregular and rare events, in time series data, can enable us to find abnormal events in any natural phenomena, which may require special treatment. Moreov…
Improves interpretability of anomaly scores in GBRBM-based detection.
Anomaly detection algorithms are often thought to be limited because they don't facilitate the process of validating results performed by domain experts. In Contrast, deep learning algorithms for anomaly detection, such as autoencoders, point out the outliers, saving experts the time-consuming task of examining normal …
Anomaly detection is the process of finding data points that deviate from a baseline. In a real-life setting, anomalies are usually unknown or extremely rare. Moreover, the detection must be accomplished in a timely manner or the risk of corrupting the system might grow exponentially. In this work, we propose a two lev…