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.
Optimization approach for efficient sampling in optical mapping for structural variant detection.
The detection of rare variants is important for understanding the genetic heterogeneity in mixed samples. Recently, next-generation sequencing (NGS) technologies have enabled the identification of single nucleotide variants (SNVs) in mixed samples with high resolution. Yet, the noise inherent in the biological processe…
Paper presents a new method to detect process differences at the trace level using mutual fingerprints.
New CUSUM algorithm detects changes in unnormalized models.
Proposes a novel anomaly detection method for echocardiogram videos.
Unsupervised two-view learning, or detection of dependencies between two paired data sets, is typically done by some variant of canonical correlation analysis (CCA). CCA searches for a linear projection for each view, such that the correlations between the projections are maximized. The solution is invariant to any lin…
This paper presents a novel deep learning based method for automatic malware signature generation and classification. The method uses a deep belief network (DBN), implemented with a deep stack of denoising autoencoders, generating an invariant compact representation of the malware behavior. While conventional signature…
The paper classifies links with low rank knot Floer and Khovanov homologies.
DNA sequencing to identify genetic variants is becoming increasingly valuable in clinical settings. Assessment of variants in such sequencing data is commonly implemented through Bayesian heuristic algorithms. Machine learning has shown great promise in improving on these variant calls, but the input for these is still…
Two new scoring methods improve anomaly detection in Isolation Forest.
Variant of Seiberg-Witten equations for multiple-spinors connects to stability of holomorphic bundles.
Method detects batch heterogeneity in genomic data.
The paper analyzes DeepWalk and node2vec for community detection in stochastic blockmodels.
Using an intuitive concept of what constitutes a meaningful community, a novel metric is formulated for detecting non-overlapping communities in undirected, weighted heterogeneous networks. This metric, modularity density, is shown to be superior to the versions of modularity density in present literature. Compared to …
Recently, the introduction of the generative adversarial network (GAN) and its variants has enabled the generation of realistic synthetic samples, which has been used for enlarging training sets. Previous work primarily focused on data augmentation for semi-supervised and supervised tasks. In this paper, we instead foc…
The study formalizes temporal precision and recall for anomaly detection in sequences.
Improved anomaly detection using adversarial mirrored autoencoders.
Improves community detection in directed networks with theoretical guarantees.
Local network community detection aims to find a single community in a large network, while inspecting only a small part of that network around a given seed node. This is much cheaper than finding all communities in a network. Most methods for local community detection are formulated as ad-hoc optimization problems. In…
Online algorithm detects community structure in dynamic event streams.
Open category detection is the problem of detecting "alien" test instances that belong to categories or classes that were not present in the training data. In many applications, reliably detecting such aliens is central to ensuring the safety and accuracy of test set predictions. Unfortunately, there are no algorithms …
Common complex diseases are likely influenced by the interplay of hundreds, or even thousands, of genetic variants. Converging evidence shows that genetic variants with low marginal effects (LME) play an important role in disease development. Despite their potential significance, discovering LME genetic variants and as…
Real-world networks usually have community structure, that is, nodes are grouped into densely connected communities. Community detection is one of the most popular and best-studied research topics in network science and has attracted attention in many different fields, including computer science, statistics, social sci…
New algorithm detects changes quickly without knowing parameters, near optimally.
Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of …
Detection of rare variants by resequencing is important for the identification of individuals carrying disease variants. Rapid sequencing by new technologies enables low-cost resequencing of target regions, although it is still prohibitive to test more than a few individuals. In order to improve cost trade-offs, it has…
A new method for detecting anomalies in large, high-dimensional data streams using probabilistic forest models.
Outlier detection methods have become increasingly relevant in recent years due to increased security concerns and because of its vast application to different fields. Recently, Pauwels and Lasserre (2016) noticed that the sublevel sets of the inverse Christoffel function accurately depict the shape of a cloud of data …
Locally private methods detect changes in time series data.
Paper proposes AdaDetect for FDR-controlled novelty detection.
Adaptive algorithm for outlier detection by balancing arm exploration and threshold estimation.
FCDD explains deep anomaly detection by mapping anomalies away and providing heatmap explanations.
Spectral algorithms solve optimal community detection and related problems.
This paper proposes the adaptation of Support Vector Data Description (SVDD) to the multiple kernel case (MK-SVDD), based on SimpleMKL. It also introduces a variant called Slim-MK-SVDD that is able to produce a tighter frontier around the data. For the sake of comparison, the equivalent methods are also developed for O…
SONAR improves outlier detection for streaming data with strong theoretical guarantees.
Detects torus knots using SL(2,C) representations and instanton Floer homology.
MPT improves CNN and energy-based models' OOD detection and generalization.
Receiver algorithms which combine belief propagation (BP) with the mean field (MF) approximation are well-suited for inference of both continuous and discrete random variables. In wireless scenarios involving detection of multiple signals, the standard construction of the combined BP-MF framework includes the equalizat…
A new method detects anomalies in trajectory data using normalizing flows.
Optimizes quickest detection of drift in Brownian motion with false negatives.
The repeated community-wide reuse of test sets in popular benchmark problems raises doubts about the credibility of reported test-error rates. Verifying whether a learned model is overfitted to a test set is challenging as independent test sets drawn from the same data distribution are usually unavailable, while other …
New model detects communities in networks with signed, continuous weights.
Optimizes latency and false alarm probability in change detection problems.
Deep learning has recently demonstrated state-of-the art performance on key tasks related to the maintenance of computer systems, such as intrusion detection, denial of service attack detection, hardware and software system failures, and malware detection. In these contexts, model interpretability is vital for administ…
Paper detects anomalies in wheat and rapeseed crops using satellite data.
ACE explains security anomaly detection models through feature contributions.
Analyzing the temporal behavior of nodes in time-varying graphs is useful for many applications such as targeted advertising, community evolution and outlier detection. In this paper, we present a novel approach, STWalk, for learning trajectory representations of nodes in temporal graphs. The proposed framework makes u…
This paper evaluates anomaly detection methods for multivariate time series data.