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.
This paper predicts stock prices during unusual events like the pandemic.
The HDoutliers algorithm is a powerful unsupervised algorithm for detecting anomalies in high-dimensional data, with a strong theoretical foundation. However, it suffers from some limitations that significantly hinder its performance level, under certain circumstances. In this article, we propose an algorithm that addr…
Special issue on understanding physical processes from unusual diffusion patterns.
ELM combines machine learning and feature engineering for anomalous diffusion detection.
Paper detects anomalous edges in social networks using edge exchangeability.
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…
Paper introduces kernel methods for detecting anomalous changes in remote sensing imagery.
DCASE 2021 ASD task tackles domain-shifted anomalous sound detection.
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 paper explores anomalous subvarieties in hyperbolic 3-manifolds and their geometric implications.
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…
Paper discusses ASD challenge for machine condition monitoring.
Bi-Mamba model predicts diffusion coefficients and exponents from short data.
Paper investigates preserving anomalous subgroups in anonymized datasets.
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…
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…
RCGAN improves anomaly detection by better recognizing anomalous samples.
Simulates financial market orders using anomalous diffusion models.
Detects audio adversarial examples using anomalous pattern detection.
Paper introduces IAD for detecting anomalous VMMs in cloud without VMM access.
The paper distinguishes between conditional and marginal processes in language models and discusses conditions for usefulness.
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…
Anomalous diffusion in SGD reveals interactions between hyperparameters and Hessian.
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…
Proposes a novel model-agnostic training procedure for anomaly detection incorporating known anomalies.
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.
A nonparametric anomalous hypothesis testing problem is investigated, in which there are totally n sequences with s anomalous sequences to be detected. Each typical sequence contains m independent and identically distributed (i.i.d.) samples drawn from a distribution p, whereas each anomalous sequence contains m i.i.d.…
The state of a stochastic process evolving over a time is typically assumed to lie on a normal distribution whose width scales like . However, processes where the probability distribution is not normal and the scaling exponent differs from are known. The search for possible origins of such "a…
Paper introduces MoM-KDE for robust density estimation robust to anomalous data.
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…
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…
An adversarial detector identifies anomalous sequences in sequential data.
A new framework detects anomalous inputs to DNNs.
This work detects anomalous clients in federated learning to prevent their adverse impacts.
Deep learning dynamics exhibit anomalous superdiffusion initially, aiding escape from local minima.
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.
VAE improves anomaly detection for jet tagging at the LHC.
A new method detects anomalous sounds using self-supervised learning.
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…
Analyzed Bitcoin market index volatility changes over two distinct periods using anomalous diffusion and multifractal analysis.
Testing procedures for predictive regressions with lagged autoregressive variables imply a suboptimal inference in presence of small violations of ideal assumptions. We propose a novel testing framework resistant to such violations, which is consistent with nearly integrated regressors and applicable to multi-predictor…
Anomaly detection identifies unusual malaria transmission patterns in Ghana.
Analyzed a generalized voter model with power-law herding intensity, revealing anomalous diffusion and long-range memory.
We describe a birational map between subvarieties in the character varieties of mutative 3-manifolds. By studying the birational map, one can decide in certain circumstances whether a mutation surface is detected by an ideal point of the character variety.
Recently, deep generative models have become increasingly popular in unsupervised anomaly detection. However, deep generative models aim at recovering the data distribution rather than detecting anomalies. Besides, deep generative models have the risk of overfitting training samples, which has disastrous effects on ano…