Bayesian detector improves defect detection on rough surfaces.
problem Defect detection on motion blurred images with rough texture.
method Developed Bayesian detector using reflected non-local prior distributions.
result Significantly enhanced accuracy in identifying defects.
Bayesian method detects outliers and uncertain points in data.
problem Detecting outliers and uncertain points in data using Bayesian methods.
method Generative model of data curation for aleatoric uncertainty, combining with epistemic uncertainty and outlier exposure.
result Principled Bayesian approach outperforms methods using aleatoric or epistemic uncertainty alone.
Bayesian nonparametric models improve OOD detection, especially with complex covariance structures.
problem Improving out-of-distribution detection methods, especially in complex scenarios.
method Proposes Bayesian nonparametric mixture models with hierarchical priors that generalize the Mahalanobis distance score.
result Bayesian nonparametric methods outperform existing OOD methods, especially in complex scenarios.
Bayesian Neural Networks improve OOD detection with limited data.
problem Limited training data hinders reliable OOD detection.
method Bayesian Neural Networks with expected logit vectors.
result Bayesian methods outperform deterministic methods in small data settings.
A conjugate Bayesian method detects change points in Hawkes processes efficiently.
problem Non-conjugacy between Hawkes process likelihood and prior causes inefficiency in change point detection.
method Data augmentation to propose a conjugate Bayesian two-step change point detection method.
result The conjugate method is more accurate and efficient than non-conjugate methods.
Paper introduces a fast, robust, scalable method for detecting changes in data streams.
problem Detecting changes in data streams efficiently and reliably.
method Bayesian online changepoint detection with provable robustness and scalability.
result The proposed method is more than 10 times faster than previous approaches and provides provable robustness.
Bayesian neural networks fail at OOD detection, revealing fundamental issues.
problem The ability of Bayesian neural networks to detect out-of-distribution data.
method Empirical study of Bayesian inference in neural networks, including infinite-width and finite-width cases using Hamiltonian Monte Carlo.
result Bayesian inference in common neural network architectures does not lead to good OOD detection.
Bayesian algorithm detects changes in data sequences in real-time.
problem Retrospective inference of change points without future consideration.
method Extends Bayesian Online Change Point Detection to predict future change points.
result Inference algorithm can handle observation models dependent on total segment duration.
BADAC combines Bayesian methods for anomaly detection and classification.
problem Statistical uncertainties in machine learning algorithms, especially for anomaly detection.
method Unified hierarchical Bayesian framework that marginalizes over unknown data values.
result BADAC outperforms standard algorithms in classification and anomaly detection with uncertainties.
NSIBF detects anomalies in CPS using neural system identification and Bayesian filtering.
problem Detecting anomalies in CPS with complex dynamics and sensor noise.
method Neural System Identification and Bayesian Filtering (NSIBF).
result NSIBF outperforms state-of-the-art methods in anomaly detection for CPS.
Proposes BATer for improved adversarial example detection.
problem Detecting adversarial examples in neural networks.
method Introduces a Bayesian adversarial example detector (BATer) using random components in a Bayesian neural network.
result BATer outperforms state-of-the-art detectors in adversarial example detection.
ABACUS detects changes in multivariate data without labels.
problem Challenging to detect changes in correlated multivariate data.
method Bayesian source separation with multi-level sparsity.
result Competitive or superior performance in simulations and real-world applications.
Bayesian Optimization improves machine learning for detecting network attacks.
problem Detecting network attacks to secure critical information.
method Bayesian Optimization for tuning machine learning algorithms (SVM-RBF, RF, k-NN).
result The proposed framework achieves high accuracy and low false alarms.
Bayesian method adapts to unknown distribution shifts in online learning.
problem Online learning with unknown and irregular distribution shifts.
method Bayesian inference with change-point detection and beam search.
result Improves adaptation to new data distributions over state-of-the-art methods.
Bayesian neural networks fail at out-of-distribution detection, revealing fundamental issues.
problem Out-of-distribution detection with Bayesian neural networks.
method Study of Bayesian inference with function space priors and comparison to Gaussian processes.
result Bayesian inference with function space priors does not lead to good OOD detection.
Bayesian autoencoders improve OOD detection by addressing Bernoulli likelihood issues.
problem Out-of-distribution (OOD) detection fails with Bernoulli likelihood for certain datasets.
method Proposes Bayesian autoencoders and alternative likelihood models to fix the issue.
result Bayesian autoencoders and alternative likelihood models improve OOD detection accuracy.
Bayesian methods detect clusters in noisy data more reliably.
problem Noisy data distorts traditional clustering methods, leading to unreliable results.
method Bayesian community detection using Minimum Description Length principle.
result Bayesian methods identify more robust clusters in noisy data.
BayPrAnoMeta tackles few-shot industrial image anomaly detection with Bayesian methods.
problem Challenges in industrial image anomaly detection, especially class imbalance and scarcity of labeled samples.
method Bayesian Proto-MAML approach with probabilistic normality models and Bayesian posterior predictive likelihood.
result Consistent and significant AUROC improvements over existing methods in few-shot anomaly detection.
Bayesian coresets help reduce network intrusion detection data size.
problem Large network intrusion detection data sets hinder Bayesian learning.
method Use Bayesian coresets to reduce data size while maintaining accuracy.
result Bayesian coresets improve learning accuracy and reduce storage needs.
This letter presents a novel Block Bayesian Hypothesis Testing Algorithm (Block-BHTA) for reconstructing block sparse signals with unknown block structures. The Block-BHTA comprises the detection and recovery of the supports, and the estimation of the amplitudes of the block sparse signal. The support detection and rec…
In this work we perform outlier detection using ensembles of neural networks obtained by variational approximation of the posterior in a Bayesian neural network setting. The variational parameters are obtained by sampling from the true posterior by gradient descent. We show our outlier detection results are comparable …
Bayesian method detects neuron spike activities from noisy fluorescence data.
problem Detecting neuron spike activities from noisy fluorescence data.
method Random finite set (RFS) based Bayesian approach.
result Gains 12% extra detection accuracy compared to MLSpike method.
Bayesian algorithm detects changes in fluctuating baselines.
problem Detecting change points in time series with a shifting baseline.
method Extended Bayesian online change point detection (BOCPD) algorithm.
result The extended algorithm can detect changes in fluctuating baselines.
BLISS detects and separates astronomical sources quickly and accurately.
problem Detecting and separating overlapping astronomical sources in large images.
method Bayesian Light Source Separator (BLISS) using deep generative models and variational inference.
result BLISS can process megapixel images in seconds and produce highly accurate catalogs.
Bayesian Recurrent Neural Networks improve fault detection and identification in manufacturing.
problem Detect and identify faults in chemical processes to ensure optimal operations.
method Bayesian Recurrent Neural Networks (BRNNs) with variational dropout.
result BRNNs provide uncertainty estimates for fault detection and identification.
Bayesian layer improves image segmentation and out-of-distribution detection.
problem Outlier detection in image segmentation.
method Parameter-efficient hierarchical convolutional Gaussian Processes in Wasserstein-2 space.
result Uncertainty estimates improve out-of-distribution detection.
Bayesian neural networks improve reliability in multimedia forensics.
problem Challenges with out-of-distribution data in multimedia authentication.
method Proposes Bayesian neural networks (BNN) for forensic tasks.
result BNNs provide distributions for better reliability and out-of-distribution detection.
Bayesian framework detects symmetries in chaotic dynamical systems.
problem Detecting symmetries in chaotic attractors for insights into dynamical system structure.
method Bayesian framework using Gibbs posterior constructed from Wasserstein distances.
result Bayesian framework accurately recovers symmetries under high noise and small sample sizes.
New algorithm detects changes in Gaussian Process covariance structures.
problem Detecting abrupt changes in Gaussian Process covariance structures.
method Statistical hypothesis tests and Bayesian Online Change Point Detection (BOCPD) with improved thresholds.
result CBOCPD finds multiple structural breaks in GPs even with imprecise hyperparameters.
Bayesian active learning improves stress and affect detection on wearable devices.
problem Handling unlabeled data in real-time for stress and affect detection.
method Bayesian Neural Networks with Monte-Carlo Dropout and suitable acquisition functions.
result Framework achieves significant efficiency boost and low number of acquired pool points.
Bayesian methods detect and forecast inclinometer anomalies in UK rail data.
problem Detecting and predicting dangerous movements in earthwork slopes.
method Bayesian UQ techniques applied to latent Markov process and non-linear Bayesian filter.
result Anomaly detection and forecasting demonstrated on large real-world data.
SVOCD detects changepoints in complex systems efficiently.
problem Detecting changepoints in complex systems efficiently.
method Stein variational online changepoint detection integrating SVN method and BOCPD.
result Successfully applied to Hawkes processes and LSTM neural networks.
A novel dynamic Bayesian nonparametric topic model for anomaly detection in video is proposed in this paper. Batch and online Gibbs samplers are developed for inference. The paper introduces a new abnormality measure for decision making. The proposed method is evaluated on both synthetic and real data. The comparison w…
Bayesian Gaussian Processes layer detects out-of-distribution data in medical imaging.
problem Detecting out-of-distribution data in medical imaging tasks.
method Parameter-efficient hierarchical convolutional Gaussian Processes in Wasserstein-2 space.
result Uncertainty estimates enable superior out-of-distribution detection compared to previous methods.
Bayesian autoencoder detects AI safety risk of out-of-distribution inputs.
problem Detecting unreliable predictions from AI models with different distributions.
method Probabilistic, unsupervised Bayesian variational autoencoder with posterior estimation.
result Effective detection of out-of-distribution inputs in both input and latent spaces.
Proposes a new method to measure epistemic uncertainty in Bayesian neural networks.
problem Measuring epistemic uncertainty in Bayesian neural networks for out-of-distribution detection.
method Proposes measuring disagreement between logits and their pre-softmax counterparts as an epistemic uncertainty measure.
result Proposed epistemic uncertainty scores outperform mutual information and equal predictive entropy performance.
Identifying changes in the generative process of sequential data, known as changepoint detection, has become an increasingly important topic for a wide variety of fields. A recently developed approach, which we call EXact Online Bayesian Changepoint Detection (EXO), has shown reasonable results with efficient computati…
This work addresses the problem of segmentation in time series data with respect to a statistical parameter of interest in Bayesian models. It is common to assume that the parameters are distinct within each segment. As such, many Bayesian change point detection models do not exploit the segment parameter patterns, whi…
We develop a supervised machine learning model that detects anomalies in systems in real time. Our model processes unbounded streams of data into time series which then form the basis of a low-latency anomaly detection model. Moreover, we extend our preliminary goal of just anomaly detection to simultaneous anomaly pre…
Bayesian Neural Networks detect gravitational wave events with high accuracy and real-time potential.
problem Detecting and identifying the full duration of compact binary coalescence events in gravitational wave data.
method Integrating Bayesian approach into a CLDNN classifier that combines CNN and LSTM for event detection and uncertainty estimation.
result Successfully detected all seven BBH events in LIGO Livingston O2 data with high accuracy.
A novel unified Bayesian framework for network detection is developed, under which a detection algorithm is derived based on random walks on graphs. The algorithm detects threat networks using partial observations of their activity, and is proved to be optimum in the Neyman-Pearson sense. The algorithm is defined by a …
Bayesian approach helps detect adversarial examples in neural networks.
problem Detecting adversarial examples in deep neural networks.
method Adversarial spheres and approximate Bayesian inference for a linear model.
result Bayesian methods provide better predictions of adversarial points.
Bayesian model predicts emotion from fitness tracker heartbeat data.
problem Predicting emotional valence from consumer fitness tracker heartbeat data.
method End-to-end Bayesian deep learning model using PPG data.
result Peak F1 score of 0.7 for emotional valence classification.
Bayesian methods improve OoD detection in deep networks.
problem Detecting Out-of-Distribution (OoD) inputs in deep neural networks.
method Three Bayesian inference approaches applied to VAE weights.
result Improved OoD detection scores over benchmarks.
Bayesian approach detects changepoints with cost-sensitive data fidelity.
problem Detecting abrupt shifts in time series data with limited resources.
method Bayesian approach with active, cost-sensitive data fidelity switching.
result Information-based approach reduces total cost while maintaining accuracy.
Anomaly Detection has several important applications. In this paper, our focus is on detecting anomalies in seller-reviewer data using tensor decomposition. While tensor-decomposition is mostly unsupervised, we formulate Bayesian semi-supervised tensor decomposition to take advantage of sparse labeled data. In addition…
GANF uses normalizing flows to detect anomalies in multiple time series.
problem Detecting anomalies in multiple time series with interdependencies.
method Bayesian network integration with normalizing flows for unsupervised anomaly detection.
result GANF effectively detects anomalies and identifies distribution drift in time series data.
Detecting anomalies in multivariate functional data using Bayesian nonparametric methods.
problem Detecting anomalies in functional data.
method Bayesian nonparametric approach with infinite mixture of multi-output Gaussian processes.
result Anomalous observations assigned to small mixture components.