Generatability in metric spaces studied with novel novelty parameters.
problem Understanding generatability in metric spaces with asymmetric novelty parameters.
method Introducing (ε,ε′)-closure dimension to characterize uniform and non-uniform generatability. result Generatability is stable across novelty scales in doubling spaces but can be highly scale-sensitive in general metric spaces.
Deep learning model for AI co-creativity boosts design creativity.
problem Design creativity enhancement through AI collaboration.
method Novelty metric applied to deep learning-generated vector representations for conceptual shifts in a co-creative design system.
result Increasing novelty in AI contributions correlates with higher creative outcomes in design.
This paper introduces CENIE to quantify environment novelty for better UED.
problem Challenges in measuring environment novelty for effective UED.
method CENIE framework using state-action space coverage and Gaussian Mixture Models.
result CENIE improves UED performance across multiple benchmarks.
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…
Robust VAE detects anomalies in corrupted data.
problem Detect anomalies in data with high corruption.
method Robust Variational Autoencoder (VAE) with four modifications.
result Establishes robustness to outliers and suitability to low-rank modeling.
DCAE learns compact latent representations for one-class novelty detection.
problem Learning compact latent representations for one-class novelty detection.
method DCAE learns compact and collapse-free latent representations through internal discriminative layers of GANs, reconstructing in-class data finely and exclusively.
result DCAE achieves state-of-the-art performance on novelty and adversarial example detection.
Proposes a novel method for detecting novelty in multi-modal data.
problem Challenges in detecting novelty in high-dimensional, multi-modal data.
method Orthogonalized latent space for disentangling features and defining novelty score.
result Proposed method outperforms state-of-the-art algorithms in novelty detection.
Study compares methods for detecting novelty in textual data streams.
problem Lack of annotated datasets for novelty detection.
method Simulation framework to create controlled datasets and benchmark methods.
result Evaluation of state-of-the-art methods on different types of novelty.
Deep learning detects novel changes in time series data.
problem Detecting novel changes in time series with unknown probability structures.
method Causally extracts an innovations sequence for novelty detection.
result Minimax optimality established for the novelty detection method.
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…
A model predicts user movie preferences based on novelty-seeking traits.
problem Accurately predicting user movie preferences for competitive websites.
method DFNSM model uses demographic, genre, and novelty-seeking data.
result DFNSM outperforms previous models in movie recommendation accuracy.
Novel methods generate diverse policies in reinforcement learning.
problem Generating diverse policies in reinforcement learning.
method Constrained optimization perspective, introducing new metrics, and novel policy generation methods.
result Improved novelty and performance of generated policies.
This work introduces a novel method to evaluate generative model novelty.
problem Evaluating the novelty of generative models compared to a reference model.
method Spectral approach to differential clustering and Kernel-based Entropic Novelty (KEN) score.
result The KEN score effectively detects novel modes and compares generative models.
CSI detects novelty by contrasting shifted instances, outperforming existing methods.
problem Detecting samples from outside the training distribution.
method Contrastive learning with distributionally shifted augmentations.
result CSI outperforms existing methods in various novelty detection scenarios.
BERT learns claim descriptions to identify patent novelty.
problem Identifying novel patent claims among existing documents.
method Training BERT on concatenated claims and descriptions, scoring BERT's output.
result BERT identifies relevant X documents for patent novelty.
We show that the two-component Hunter-Saxton system with negative coupling constant describes the geodesic flow on an infinite-dimensional pseudosphere. This approach yields explicit solution formulae for the Hunter-Saxton system. Using this geometric intuition, we conclude by constructing global weak solutions. The ma…
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…
Novelty search in low-dimensional space improves sample efficiency in exploration tasks.
problem Efficient exploration in complex environments with sparse rewards.
method Combines model-based and model-free objectives to learn a low-dimensional representation. Uses intrinsic novelty rewards based on nearest neighbor distances in this space.
result Our approach achieves more sample-efficient exploration compared to strong baselines on various tasks.
News novelty predicts negative stock market returns.
problem Negative stock market returns due to increased news novelty.
method Quantified news novelty using entropy measure from recurrent neural network applied to a large news corpus.
result Entropy exposure carries a negative risk premium, indicating that assets positively correlated with entropy hedge aggregate news risk.
Paper proposes AdaDetect for FDR-controlled novelty detection.
problem Semi-supervised novelty detection with probabilistic classification.
method Data-adaptive learning of transformation to control FDR.
result Control of false discovery rate on detected novelties.
We show how to define Riemannian metrics and connections on a noncommutative torus in such a way that an analogue of Levi-Civita's theorem on the existence and uniqueness of a Riemannian connection holds. The major novelty is that we need to use two different notions of noncommutative vector field. Levi-Civita's theore…
This article contains a detailed study, in the toric case, of the test configuration geodesic rays defined by Phong-Sturm. We show that the `Bergman approximations' of Phong-Sturm converge in C^1 to the geodesic ray and that the geodesic ray itself is C^{1,1} and no better. The \kahler metrics associated to the geodesi…
We show the optimal C1,1 regularity of geodesics in nef and big cohomology class on Kähler manifolds away from the non-Kähler locus, assuming sufficiently regular initial data. As a special case, we prove the C1,1 regularity of geodesics of Kähler metrics on compact Kähler varieties away from the singular loc…
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.
problem Challenges of static distribution in novelty detection.
method Semi-supervised network architecture with auxiliary labels for contextual information.
result Single model achieves performance of individually trained models on various contexts.
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…
The strong unique continuation property for Einstein metrics can be concluded from the well-known fact that Einstein metrics are analytic in geodesic normal coordinates. Here we give a proof of the same result that given two Einstein metrics with the same Ricci curvature on a fixed manifold, if they agree to infinite o…
This paper improves neural architecture search by focusing on novelty-driven sampling.
problem Lack of positive correlation between validation accuracy and test accuracy in One-Shot NAS.
method Single-path supernet with optimized weights and novelty search for architecture sampling.
result Novelty search method achieves state-of-the-art test error rate on CIFAR-10.
Proposes UICR to improve novelty in recommendation systems without sacrificing relevance.
problem Balancing relevance and novelty in recommendation systems is challenging, especially for long-tail items.
method Introduces uncertainty modeling in the matching stage and multi-task modeling of model and index uncertainty.
result Improves novelty without sacrificing relevance, as shown by experimental results and online A/B tests.
In this paper, we consider very rough solutions to Cauchy problem for the Einstein vacuum equations in CMC spacial harmonic gauge, and obtain the local well-posedness result in Hs,s>2. The novelty of our approach lies in that, without resorting to the standard paradifferential regularization over the rough, Einstei…
Study on robustness of learning-based novelty detection methods under adversarial attacks.
problem Understanding how learning-based novelty detection methods perform under adversarial perturbations.
method Formulated an oracle attack setup and evaluated vulnerability using black-box adversarial algorithms.
result Adversarial perturbations can significantly increase FDR while maintaining high detection power.
Article constructs coassociative submanifolds in Joyce's G2-manifolds.
problem Constructing coassociative submanifolds in Joyce's G2-manifolds. method Using Joyce's generalised Kummer construction, focusing on the critical region where the metric degenerates.
result The volume of the coassociatives shrinks to zero as the orbifold-limit is approached.
OCmst detects anomalies using CNN features and MSTs.
problem Novelty detection in data with no outliers.
method Uses CNN for feature extraction and MSTs for graph-based modeling.
result Achieved state-of-the-art results on CIFAR10 dataset.
Study small eigenvalues of Riemann surfaces degenerating with Kähler metrics.
problem Determining small eigenvalues of the Laplacian on degenerating Riemann surfaces.
method Combining heat kernel estimates and Quillen metrics to compute asymptotic behavior of eigenvalues.
result Explicit calculation of small eigenvalues as a function of the parameter.
The paper develops methods for novelty detection on path space using signature-based statistics.
problem Novelty detection on path space as a hypothesis testing problem.
method Signature-based test statistics, transportation-cost inequalities, CVaR, one-class SVM algorithms.
result Established lower bounds on type-II error and general power bounds. A new method for student-initiated action advice using novelty detection.
problem Exploration and sample inefficiency in RL, especially with teacher absence.
method Random Network Distillation (RND) to measure advice novelty, updates only for advised states.
result Significant performance improvement over state-of-the-art methods, especially in challenging scenarios.
Decentralized detection avoids sharing data, controls false discoveries.
problem Global false discovery rate control in decentralized novelty detection.
method Quantized surrogate models for low-precision sharing, preserving exchangeability.
result Quantized composite scores maintain competitive statistical power with reduced communication.
Solves a conjecture about hyperKähler manifolds using quaternionic Monge-Ampère equation.
problem Proving the solvability of a conjecture about hyperKähler manifolds with torsion.
method Solving quaternionic Monge-Ampère equation on hyperKähler manifolds without assuming flatness.
result Proves the conjecture for hyperKähler manifolds with trivial canonical bundle.
NN-EVCLUS uses neural networks to cluster data with uncertainty.
problem Clustering data with uncertainty and handling outliers.
method NN-EVCLUS learns a neural network to map attributes to mass functions, minimizing discrepancy between dissimilarities and conflict.
result NN-EVCLUS outperforms existing methods in clustering tasks.
New methods control false discoveries near the boundary in conformal novelty detection.
problem Over-optimistic assessments near the rejection threshold in conformal novelty detection.
method Support line (SL) correction and alternative procedures to control boundary false discovery rate (bFDR).
result New procedures control the boundary false discovery rate (bFDR) in the conformal setting.
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 Riemannian metric bundles and their connections to K-theory.
problem Understanding geometry and topology of manifolds with Riemannian metrics.
method Develop rigorous theory of Riemannian metric bundles and apply to K-theory.
result Contribute to deeper understanding of manifold geometry and topology.
SVD-RND detects blurred images better than conventional methods.
problem Blurred images can fool conventional OOD detection schemes.
method Constructs a novel RND-based detector that uses blurred images during training.
result SVD-RND outperforms baseline detectors in various domains.
Builds a novel educational recommender for lifelong learners.
problem Challenges in creating scalable and transparent models for lifelong learning.
method Integrative approach combining content novelty, background knowledge, and learner engagement.
result TrueLearn achieves promising performance while retaining a human interpretable learner model.
We analyse second order (in Riemann curvature) geometric flows (un-normalised) on locally homogeneous three manifolds and look for specific features through the solutions (analytic whereever possible, otherwise numerical) of the evolution equations. Several novelties appear in the context of scale factor evolution, fix…
Paper constructs new non-Anosov Partially Hyperbolic Geodesic flows using conformal deformations.
problem Creating new non-Anosov Partially Hyperbolic Geodesic flows.
method Using conformal deformations to produce examples of partially hyperbolic geodesic flows.
result Proves ergodicity for the Liouville measure and uniqueness of the measure of maximal entropy.
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.
problem Efficient novelty-based exploration in nonstationary RL environments.
method Non-parametric clustering, online density estimation, inverse dynamics loss.
result RECODE achieves state-of-the-art performance in challenging RL tasks.