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.
Deep learning detects novel changes in time series data.
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…
Proposes a novel method for detecting novelty in multi-modal data.
CSI detects novelty by contrasting shifted instances, outperforming existing methods.
In machine learning, novelty detection is the task of identifying novel unseen data. During training, only samples from the normal class are available. Test samples are classified as normal or abnormal by assignment of a novelty score. Here we propose novelty detection methods based on training variational autoencoders…
Paper proposes AdaDetect for FDR-controlled novelty detection.
Flow-based deep generative models learn data distributions by transforming a simple base distribution into a complex distribution via a set of invertible transformations. Due to the invertibility, such models can score unseen data samples by computing their exact likelihood under the learned distribution. This makes fl…
Proposes a context-aware approach to deep autoencoder novelty detection.
Since datasets with annotation for novelty at the document and/or word level are not easily available, we present a simulation framework that allows us to create different textual datasets in which we control the way novelty occurs. We also present a benchmark of existing methods for novelty detection in textual data s…
Point patterns are sets or multi-sets of unordered elements that can be found in numerous data sources. However, in data analysis tasks such as classification and novelty detection, appropriate statistical models for point pattern data have not received much attention. This paper proposes the modelling of point pattern…
OCmst detects anomalies using CNN features and MSTs.
Decentralized detection avoids sharing data, controls false discoveries.
Study on robustness of learning-based novelty detection methods under adversarial attacks.
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 …
The paper develops methods for novelty detection on path space using signature-based statistics.
AutoSciDACT detects scientific anomalies in noisy data.
New methods control false discoveries near the boundary in conformal novelty detection.
Deep learning improves anomaly detection across various fields.
DCAE learns compact latent representations for one-class novelty detection.
The Familiarity Hypothesis explains deep open set methods' success in detecting novel objects.
Paper tackles novelty detection in text classification.
Conventional out-of-distribution (OOD) detection schemes based on variational autoencoder or Random Network Distillation (RND) have been observed to assign lower uncertainty to the OOD than the target distribution. In this work, we discover that such conventional novelty detection schemes are also vulnerable to the blu…
Robust VAE detects anomalies in corrupted data.
Machine-learning driven safety-critical autonomous systems, such as self-driving cars, must be able to detect situations where its trained model is not able to make a trustworthy prediction. Often viewed as a black-box, it is non-obvious to determine when a model will make a safe decision and when it will make an erron…
(ABRIDGED) In previous work, two platforms have been developed for testing computer-vision algorithms for robotic planetary exploration (McGuire et al. 2004b,2005; Bartolo et al. 2007). The wearable-computer platform has been tested at geological and astrobiological field sites in Spain (Rivas Vaciamadrid and Riba de S…
Generative Kernel PCA explores latent spaces for data interpretation and novelty detection.
Extends Mahalanobis distance to Banach spaces for anomaly detection.
This work introduces a novel method to evaluate generative model novelty.
New deep probabilistic model handles missing data in time series forecasting.
SAGE improves memory efficiency by selectively adding, merging, or ignoring new facts.
Deep neural networks have achieved impressive success in large-scale visual object recognition tasks with a predefined set of classes. However, recognizing objects of novel classes unseen during training still remains challenging. The problem of detecting such novel classes has been addressed in the literature, but mos…
A novel outlier score detects new road infrastructure images.
Many deep models have been recently proposed for anomaly detection. This paper presents comparison of selected generative deep models and classical anomaly detection methods on an extensive number of non--image benchmark datasets. We provide statistical comparison of the selected models, in many configurations, archite…
KOD detects outliers in high-dimensional data.
A new method for student-initiated action advice using novelty detection.
This paper presents an innovative and generic deep learning approach to monitor heart conditions from ECG signals.We focus our attention on both the detection and classification of abnormal heartbeats, known as arrhythmia. We strongly insist on generalization throughout the construction of a deep-learning model that tu…
We study sequential change-point detection procedures based on linear sketches of high-dimensional signal vectors using generalized likelihood ratio (GLR) statistics. The GLR statistics allow for an unknown post-change mean that represents an anomaly or novelty. We consider both fixed and time-varying projections, deri…
NN-EVCLUS uses neural networks to cluster data with uncertainty.
RAID algorithm detects anomalies in real-time IoT systems.
Detecting edge correlation between two graphs sharpens a threshold based on densest subgraph.
Biodiversity monitoring using audio recordings is achievable at a truly global scale via large-scale deployment of inexpensive, unattended recording stations or by large-scale crowdsourcing using recording and species recognition on mobile devices. The ability, however, to reliably identify vocalising animal species is…
We present a new machine learning and text information extraction approach to detection of cyber threat events in Twitter that are novel (previously non-extant) and developing (marked by significance with respect to similarity with a previously detected event). While some existing approaches to event detection measure …
This work bridges outlier and drift detection by comparing inputs to a part of the reference distribution.
This paper introduces CENIE to quantify environment novelty for better UED.
Automated detection of new, interesting, unusual, or anomalous images within large data sets has great value for applications from surveillance (e.g., airport security) to science (observations that don't fit a given theory can lead to new discoveries). Many image data analysis systems are turning to convolutional neur…
Generatability in metric spaces studied with novel novelty parameters.
NN-CUSUM detects changes in high-dimensional data using neural networks.
As more and more people shift their movie watching online, competition between movie viewing websites are getting more and more intense. Therefore, it has become incredibly important to accurately predict a given user's watching list to maximize the chances of keeping the user on the platform. Recent studies have sugge…