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 a novel method for detecting novelty in multi-modal data.
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…
This paper introduces CENIE to quantify environment novelty for better UED.
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…
Generatability in metric spaces studied with novel novelty parameters.
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…
This work introduces a novel method to evaluate generative model novelty.
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…
CSI detects novelty by contrasting shifted instances, outperforming existing methods.
BERT learns claim descriptions to identify patent novelty.
Novelty search in low-dimensional space improves sample efficiency in exploration tasks.
News novelty predicts negative stock market returns.
This paper presents a computational model for conceptual shifts, based on a novelty metric applied to a vector representation generated through deep learning. This model is integrated into a co-creative design system, which enables a partnership between an AI agent and a human designer interacting through a sketching c…
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.
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…
Proposes UICR to improve novelty in recommendation systems without sacrificing relevance.
Study on robustness of learning-based novelty detection methods under adversarial attacks.
OCmst detects anomalies using CNN features and MSTs.
The paper develops methods for novelty detection on path space using signature-based statistics.
A new method for student-initiated action advice using novelty detection.
Decentralized detection avoids sharing data, controls false discoveries.
New methods control false discoveries near the boundary in conformal novelty detection.
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 …
In this paper, we focus on a prediction-based novelty estimation strategy upon the deep reinforcement learning (DRL) framework, and present a flow-based intrinsic curiosity module (FICM) to exploit the prediction errors from optical flow estimation as exploration bonuses. We propose the concept of leveraging motion fea…
RECODE uses clustering and embedding to track state visitation counts in RL.
AutoSciDACT detects scientific anomalies in noisy data.
One-Shot Neural architecture search (NAS) attracts broad attention recently due to its capacity to reduce the computational hours through weight sharing. However, extensive experiments on several recent works show that there is no positive correlation between the validation accuracy with inherited weights from the supe…
BEACON optimizes discovery by efficiently finding novel behaviors.
Paper tackles novelty detection in text classification.
SAGE improves memory efficiency by selectively adding, merging, or ignoring new facts.
New deep probabilistic model handles missing data in time series forecasting.
Robust VAE detects anomalies in corrupted data.
Plan2Explore learns new tasks efficiently through self-supervised planning.
(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.
Novel methods generate diverse policies in reinforcement learning.
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…
One of the most ambitious use cases of computer-assisted learning is to build a recommendation system for lifelong learning. Most recommender algorithms exploit similarities between content and users, overseeing the necessity to leverage sensible learning trajectories for the learner. Lifelong learning thus presents un…
A major challenge in reinforcement learning is exploration, when local dithering methods such as epsilon-greedy sampling are insufficient to solve a given task. Many recent methods have proposed to intrinsically motivate an agent to seek novel states, driving the agent to discover improved reward. However, while state-…
The Familiarity Hypothesis explains deep open set methods' success in detecting novel objects.
We investigate the use of attentional neural network layers in order to learn a `behavior characterization' which can be used to drive novelty search and curiosity-based policies. The space is structured towards answering a particular distribution of questions, which are used in a supervised way to train the attentiona…
Deep learning improves anomaly detection across various fields.
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…
Extends Mahalanobis distance to Banach spaces for anomaly detection.
In one-class novelty detection, a model learns solely on the in-class data to single out out-class instances. Autoencoder (AE) variants aim to compactly model the in-class data to reconstruct it exclusively, thus differentiating the in-class from out-class by the reconstruction error. However, compact modeling in an im…