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.
Paper introduces kernel methods for detecting anomalous changes in remote sensing imagery.
From a sequence of similarity networks, with edges representing certain similarity measures between nodes, we are interested in detecting a change-point which changes the statistical property of the networks. After the change, a subset of anomalous nodes which compares dissimilarly with the normal nodes. We study a sim…
DCASE 2021 ASD task tackles domain-shifted anomalous sound detection.
Optimal search for change point anomaly in multiple processes.
FOCuS detects changes in mean from high-frequency data efficiently.
Paper detects anomalous edges in social networks using edge exchangeability.
RAID algorithm detects anomalies in real-time IoT systems.
This work views neural networks as data generating systems and applies anomalous pattern detection techniques on that data in order to detect when a network is processing an anomalous input. Detecting anomalies is a critical component for multiple machine learning problems including detecting adversarial noise. More br…
RCGAN improves anomaly detection by better recognizing anomalous samples.
Detects audio adversarial examples using anomalous pattern detection.
Study models systemic risks in BRICS banks under geopolitical shocks.
We propose an algorithm for detecting patterns exhibited by anomalous clusters in high dimensional discrete data. Unlike most anomaly detection (AD) methods, which detect individual anomalies, our proposed method detects groups (clusters) of anomalies; i.e. sets of points which collectively exhibit abnormal patterns. I…
Paper introduces IAD for detecting anomalous VMMs in cloud without VMM access.
Paper discusses ASD challenge for machine condition monitoring.
This paper proposes a novel optimization principle and its implementation for unsupervised anomaly detection in sound (ADS) using an autoencoder (AE). The goal of unsupervised-ADS is to detect unknown anomalous sound without training data of anomalous sound. Use of an AE as a normal model is a state-of-the-art techniqu…
The nonparametric problem of detecting existence of an anomalous interval over a one dimensional line network is studied. Nodes corresponding to an anomalous interval (if exists) receive samples generated by a distribution q, which is different from the distribution p that generates samples for other nodes. If anomalou…
Identifying anomalous patterns in real-world data is essential for understanding where, when, and how systems deviate from their expected dynamics. Yet methods that separately consider the anomalousness of each individual data point have low detection power for subtle, emerging irregularities. Additionally, recent dete…
A new framework detects anomalous inputs to DNNs.
A wide variety of application domains are concerned with data consisting of entities and their relationships or connections, formally represented as graphs. Within these diverse application areas, a common problem of interest is the detection of a subset of entities whose connectivity is anomalous with respect to the r…
Proposes a novel model-agnostic training procedure for anomaly detection incorporating known anomalies.
ELM combines machine learning and feature engineering for anomalous diffusion detection.
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…
Paper uses DBSCAN variation to detect ship anomalies.
Cliques, or fully connected subgraphs, are among the most important and well-studied graph motifs in network science. We consider the problem of finding a statisti- cally anomalous clique hidden in a large network. There are two parts to this problem: (1) detection, i.e., determining whether an anomalous clique is pres…
New method explains anomalies in multivariate time series data.
PPC detects anomalies in high-dimensional data efficiently.
A new method detects anomalous sounds using self-supervised learning.
Nonparametric detection of existence of an anomalous structure over a network is investigated. Nodes corresponding to the anomalous structure (if one exists) receive samples generated by a distribution q, which is different from a distribution p generating samples for other nodes. If an anomalous structure does not exi…
LAD detects anomalies in dynamic graphs using Laplacian matrix.
The paper presents anomaly detection in time series data using InfluxDB and Python.
Anomaly detection is not an easy problem since distribution of anomalous samples is unknown a priori. We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous sample…
Solves dual imbalance in detecting sparse anomalies in MIL.
DCASE 2022 Task 2 tackles domain shifts in ASD for machine condition monitoring.
VAE improves anomaly detection for jet tagging at the LHC.
This paper introduces a new dataset called "ToyADMOS" designed for anomaly detection in machine operating sounds (ADMOS). To the best our knowledge, no large-scale datasets are available for ADMOS, although large-scale datasets have contributed to recent advancements in acoustic signal processing. This is because anoma…
Co-TSFA improves time series forecasting by distinguishing between short-lived and persistent anomalies.
Detecting anomalous activity in human mobility data has a number of applications including road hazard sensing, telematic based insurance, and fraud detection in taxi services and ride sharing. In this paper we address two challenges that arise in the study of anomalous human trajectories: 1) a lack of ground truth dat…
Most real-world networks exhibit community structure, a phenomenon characterized by existence of node clusters whose intra-edge connectivity is stronger than edge connectivities between nodes belonging to different clusters. In addition to facilitating a better understanding of network behavior, community detection fin…
Automates defect detection using autoencoders on normal images only.
In federated learning systems, clients are autonomous in that their behaviors are not fully governed by the server. Consequently, a client may intentionally or unintentionally deviate from the prescribed course of federated model training, resulting in abnormal behaviors, such as turning into a malicious attacker or a …
This paper analyzes several interest rates time series from the United Kingdom during the period 1999 to 2014. The analysis is carried out using a pioneering statistical tool in the financial literature: the complexity-entropy causality plane. This representation is able to classify different stochastic and chaotic reg…
Paper proposes SDS for 5G security using machine learning.
Anomaly detection identifies unusual malaria transmission patterns in Ghana.
Data-driven anomaly detection methods suffer from the drawback of detecting all instances that are statistically rare, irrespective of whether the detected instances have real-world significance or not. In this paper, we are interested in the problem of specifically detecting anomalous instances that are known to have …
ResGCN detects anomalies in attributed networks by capturing sparsity and nonlinearity.
A new framework uses information theory to detect anomalies in images without labeled data.
Anomaly detection for high-dimensional data using large deviations principle.
New dataset for industrial machine malfunction detection with domain shifts.