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.
Proposes OC4Seq for detecting anomalies in discrete event sequences.
Reduces change detection to estimation using confidence sequences.
A new method detects changes in data sequences by comparing backward and forward confidence sequences.
Enhances social spam detection using multi-level dependency of relational sequences.
Non-parametric estimators improve quickest changepoint detection under irregular sequence lengths.
The problem of universal outlying sequence detection is studied, where the goal is to detect outlying sequences among sequences of samples. A sequence is considered as outlying if the observations therein are generated by a distribution different from those generating the observations in the majority of the sequenc…
Detects figure-eight knot using Khovanov homology.
Payment card fraud causes multibillion dollar losses for banks and merchants worldwide, often fueling complex criminal activities. To address this, many real-time fraud detection systems use tree-based models, demanding complex feature engineering systems to efficiently enrich transactions with historical data while co…
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this framework, we model a sequence of credit card transactions from three different perspect…
New method detects RNA modifications without prior training, revealing novel sites.
We consider the sequential anomaly detection problem in the one-class setting when only the anomalous sequences are available and propose an adversarial sequential detector by solving a minimax problem to find an optimal detector against the worst-case sequences from a generator. The generator captures the dependence i…
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this article, we model a sequence of credit card transactions from three different perspectiv…
Prime homology detects split links in prime characteristic.
ADSAGE detects anomalies in graph edge sequences for insider threat detection.
Develops a new point process model for detecting neural spike sequences.
Paper optimizes change-point detection using learned distributions from training sequences.
Deep learning detects novel changes in time series data.
IAE extracts innovations sequences for non-Gaussian processes.
Study instanton Floer homology for links in RP^3 and use it to detect knots.
LogAnMeta detects anomalies from log events using meta learning.
An unsupervised anomaly detection method for irregularly sampled time-series data.
A novel multi-resolution cluster detection (MCD) method is proposed to identify irregularly shaped clusters in space. Multi-scale test statistic on a single cell is derived based on likelihood ratio statistic for Bernoulli sequence, Poisson sequence and Normal sequence. A neighborhood variability measure is defined to …
Continuous-time event sequences represent discrete events occurring in continuous time. Such sequences arise frequently in real-life. Usually we expect the sequences to follow some regular pattern over time. However, sometimes these patterns may be interrupted by unexpected absence or occurrences of events. Identificat…
We study the problem of detecting change points (CPs) that are characterized by a subset of dimensions in a multi-dimensional sequence. A method for detecting those CPs can be formulated as a two-stage method: one for selecting relevant dimensions, and another for selecting CPs. It has been difficult to properly contro…
In this paper, we compare different types of Recurrent Neural Network (RNN) Encoder-Decoders in anomaly detection viewpoint. We focused on finding the model that can learn the same data more effectively. We compared multiple models under the same conditions, such as the number of parameters, optimizer, and learning rat…
Process Monitoring involves tracking a system's behaviors, evaluating the current state of the system, and discovering interesting events that require immediate actions. In this paper, we consider monitoring temporal system state sequences to help detect the changes of dynamic systems, check the divergence of the syste…
We prove that the reduced 2-coloured Khovanov homology detects the trefoil, using a spectral sequence to knot Floer homology.
HGConv uses HRR to efficiently detect malware, outperforming existing methods.
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 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…
The identification of anomalies in temporal data is a core component of numerous research areas such as intrusion detection, fault prevention, genomics and fraud detection. This article provides an experimental comparison of the novelty detection problem applied to discrete sequences. The objective of this study is to …
Study on signal detection in heteroscedastic Gaussian sequences with sparse alternatives.
Unsupervised model detects healthcare fraud from patient visit data.
Learned factor graphs improve inference from time sequences using neural networks.
Paper proposes semi-supervised learning using change points for sequence classification.
We introduce two invariants called sl(3) Khovanov module and pointed sl(3) Khovanov homology for spatial webs (bipartite trivalent graphs). Those invariants are related to Kronheimer-Mrowka's instanton invariants and for spatial webs by two spectral sequences. As an application of the spectral seq…
In this work we introduce malware detection from raw byte sequences as a fruitful research area to the larger machine learning community. Building a neural network for such a problem presents a number of interesting challenges that have not occurred in tasks such as image processing or NLP. In particular, we note that …
The study formalizes temporal precision and recall for anomaly detection in sequences.
Online detection of instantaneous changes in the generative process of a data sequence generally focuses on retrospective inference of such change points without considering their future occurrences. We extend the Bayesian Online Change Point Detection algorithm to also infer the number of time steps until the next cha…
Paper tackles counterfactual sentence detection and evaluation.
Uncertainty estimation is important for ensuring safety and robustness of AI systems. While most research in the area has focused on un-structured prediction tasks, limited work has investigated general uncertainty estimation approaches for structured prediction. Thus, this work aims to investigate uncertainty estimati…
MINN-SA enhances cancer detection using TCR sequences with better interpretability.
Novelty detection is the unsupervised problem of identifying anomalies in test data which significantly differ from the training set. Novelty detection is one of the classic challenges in Machine Learning and a core component of several research areas such as fraud detection, intrusion detection, medical diagnosis, dat…
Detects knots in thickened surfaces using instanton homology.
Bayesian method detects Markov order in network paths more reliably.
SoccerCPD detects tactical changes in soccer matches using spatiotemporal tracking data.
New approach reduces malware detection memory requirements and speeds up training.
FraudTransformer detects payment fraud by preserving event order and time gaps.