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.
A new method assigns anomaly scores to features for better interpretation.
Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing deep anomaly detection methods, which focus on learning new feature representations to enable downstream anomaly detection methods, perform in…
Recent semi-supervised anomaly detection methods that are trained using small labeled anomaly examples and large unlabeled data (mostly normal data) have shown largely improved performance over unsupervised methods. However, these methods often focus on fitting abnormalities illustrated by the given anomaly examples on…
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…
Study identifies high-density anomalies in normal data regions.
Improves relevancy of black-box anomaly detectors with user feedback.
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…
TPA-AD detects axle-box bearing anomalies using pseudo anomalies near normal boundaries.
New anomaly estimator reduces bias in MLE for normally distributed data.
FAMDAD detects anomalies in mixed data using kurtosis-weighted Factor Analysis.
Anomaly detection is a classical problem where the aim is to detect anomalous data that do not belong to the normal data distribution. Current state-of-the-art methods for anomaly detection on complex high-dimensional data are based on the generative adversarial network (GAN). However, the traditional GAN loss is not d…
SL(2,Z) forms lead to new anomaly formulas.
Enhanced neural networks detect thin boundaries between different types of anomalies.
AEGR method improves anomaly detection in autoencoders without needing anomaly-free training data.
Ensemble method detects time series anomalies without preselecting parameter values.
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 …
This article provides a thorough meta-analysis of the anomaly detection problem. To accomplish this we first identify approaches to benchmarking anomaly detection algorithms across the literature and produce a large corpus of anomaly detection benchmarks that vary in their construction across several dimensions we deem…
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…
A framework combines unsupervised and semi-supervised AD using synthetic anomalies.
Anomalies are intuitively easy for human experts to understand, but they are hard to define mathematically. Therefore, in order to have performance guarantees in unsupervised anomaly detection, priors need to be assumed on what the anomalies are. By contrast, active learning provides the necessary priors through approp…
EHBOS enhances HBOS by capturing feature interactions, improving anomaly detection.
Study examines how discretization affects anomaly detection in datasets.
LogAnMeta detects anomalies from log events using meta learning.
An algorithm detects anomalies based on human perception principles.
In this paper, by combining modular forms and characteristic forms, we obtain general anomaly cancellation formulas of any dimension. For dimensional manifolds, our results include the gravitational anomaly cancellation formulas of Alvarez-Gaumé and Witten in dimensions 2, 6 and 10 (\cite{AW}) as special cases. …
Improves anomaly detection with contaminated unlabeled data.
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…
Python package 'nonconform' simplifies conformal anomaly detection.
Proposes a new model for online anomaly detection in multivariate time series.
In this paper, we present Paranom, a parallel anomaly dataset generator. We discuss its design and provide brief experimental results demonstrating its usefulness in improving the classification correctness of LSTM-AD, a state-of-the-art anomaly detection model.
We address an anomaly detection setting in which training sequences are unavailable and anomalies are scored independently of temporal ordering. Current algorithms in anomaly detection are based on the classical density estimation approach of learning high-dimensional models and finding low-probability events. These al…
Anomaly detection is a significant problem faced in several research areas. Detecting and correctly classifying something unseen as anomalous is a challenging problem that has been tackled in many different manners over the years. Generative Adversarial Networks (GANs) and the adversarial training process have been rec…
Generative adversarial networks have been able to generate striking results in various domains. This generation capability can be general while the networks gain deep understanding regarding the data distribution. In many domains, this data distribution consists of anomalies and normal data, with the anomalies commonly…
Proposes a fair deep anomaly detection method.
In many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. One important way to configure the detector is by providing true labels (nominal or anomaly) for a few instances. Recent work on …
iMondrian forest combines isolation forest and Mondrian forest for better anomaly detection.
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…
TimeVQVAE-AD detects anomalies in time series data with high accuracy and provides explainable results.
CRATOS clusters time series for efficient anomaly detection.
TAnoGan detects anomalies in time series data using GANs.
A new model classifies surface anomalies in 3D point cloud data.
New study on time series anomaly detection shows overlapping inference improves performance.
A new method optimizes anomaly scoring from score distribution to improve AD performance.
Anomaly detection is a classical but worthwhile problem, and many deep learning-based anomaly detection algorithms have been proposed, which can usually achieve better detection results than traditional methods. In view of reconstruct ability of the model and the calculation of anomaly score, this paper proposes a time…
Deep RL detects anomalies from few labeled examples and large unlabeled data.
We propose a supervised anomaly detection method for data with inexact anomaly labels, where each label, which is assigned to a set of instances, indicates that at least one instance in the set is anomalous. Although many anomaly detection methods have been proposed, they cannot handle inexact anomaly labels. To measur…
MEMGAN uses memory to improve anomaly detection by isolating abnormal data.
Paper proposes a controllable RANSAC method for anomaly detection.