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.
Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring systems based on the accelerometer have been proposed for the fall detection. However,…
This paper improves hierarchical community detection efficiency using local structural properties.
problem Efficiency of hierarchical community detection methods in large networks.
method Use of local structural network properties as proxies to improve efficiency.
result Achieves competitive results in modularity with improved efficiency.
Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency, and subjective human factors. We developed a novel data-driven geological feature detection approach based on pre-stack seismic measurements. Our detection method employs a…
Online-iForest detects anomalies in streaming data efficiently.
problem Offline anomaly detection methods are impractical for streaming contexts.
method Online-iForest tracks evolving data processes in real-time without periodic retraining.
result Online-iForest outperforms all competitors in efficiency.
Efficient method for vertex embedding and community detection.
problem Vertex embedding and community detection.
method Normalized one-hot graph encoder and rank-based cluster size measure.
result Excellent numerical performance of graph encoder ensemble algorithm.
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 …
GraN detects adversarial and misclassified examples efficiently.
problem Detecting adversarial and misclassified examples in deep neural networks.
method GraN uses the layer-wise norm of the DNN's gradient regarding the loss of the current input-output combination.
result GraN achieves state-of-the-art performance on numerous problem set-ups.
A novel unsupervised outlier detection method using Randomized PCA Forest.
problem Unsupervised outlier detection in datasets.
method Randomized Principal Component Analysis (RPCA) Forest for deriving an outlier score.
result Superior performance compared to classical and state-of-the-art methods.
New CUSUM method detects changes in Hawkes networks efficiently.
problem Detecting abrupt changes in Hawkes networks.
method Efficient online recursive implementation of CUSUM statistic.
result Proposed CUSUM method outperforms existing methods.
New method detects symmetries beyond affine transformations.
problem Current methods limit symmetry detection to affine transformations.
method Framework for discovering continuous symmetry beyond affine transformations.
result Method is competitive for large sample sizes and superior for small sample sizes.
Paper proposes TBSD for efficient anomaly detection in textured images.
problem Challenges in anomaly detection for textured images, especially in manufacturing systems.
method Texture basis integrated smooth decomposition (TBSD) approach.
result TBSD surpasses benchmarks with less misidentification and superior performance.
Deep neural networks have demonstrated state-of-the-art performance on many classification tasks. However, they have no inherent capability to recognize when their predictions are wrong. There have been several efforts in the recent past to detect natural errors but the suggested mechanisms pose additional energy requi…
CBiGAN combines GANs and AutoEncoders for efficient anomaly detection in images.
problem Efficient anomaly detection in images, especially texture-type anomalies.
method CBiGAN integrates a consistency constraint in both encoder and decoder of a BiGAN model.
result CBiGAN outperforms standard baselines and state-of-the-art approaches on MVTec AD benchmark.
This paper proposes a communication-efficient deep anomaly detection framework for industrial IoT.
problem Accurately detecting anomalies in time-series data from edge devices in industrial IoT.
method A federated learning-based approach with an Attention Mechanism-based Convolutional Neural Network-Long Short Term Memory (AMCNN-LSTM) model and gradient compression.
result The proposed framework accurately and timely detects anomalies with reduced communication overhead.
Study efficient resource allocation for detecting extreme values.
problem Efficiently allocate limited resources to detect extreme values in various fields.
method Proposes ExtremeHunter algorithm for sequential resource allocation under limited feedback.
result Demonstrates ExtremeHunter outperforms oracle policy in detecting extreme values.
New Riemannian geometry for Compound Gaussian distributions applied to efficient change detection.
problem Change detection in multivariate image times series.
method Developed a recursive approach based on Riemannian optimization.
result Optimal performance achieved with computational efficiency.
COPOD detects outliers efficiently and interpretable using copulas.
problem Outliers in multivariate data are hard to detect efficiently and interpretably.
method COPOD constructs an empirical copula to predict tail probabilities and identify outliers.
result COPOD outperforms existing methods in most cases and is computationally efficient.
Hashing detects anomalies in structured data efficiently.
problem Identifying non-conforming samples on low-dimensional manifolds.
method Locality Sensitive Hashing in Preference Space.
result State-of-the-art performance at lower computational cost.
Proposes a transformer-based approach for anomaly detection in time series data.
problem Inadequate evaluation metrics and inability to capture temporal features in time series anomaly detection.
method Introduces a proper evaluation metric and proposes a transformer-based approach for anomaly detection in time series data.
result Transformer-based approach outperforms state-of-the-art detectors in detecting sequential anomalies.
A new model detects anomalies in time series data efficiently.
problem Detect anomalies in high-dimensional time series data.
method r-ssGPFA, an unsupervised online anomaly detection model using state space Gaussian processes.
result The model detects anomalies efficiently and is computationally cheaper.
Efficiently fine-tunes patient-independent seizure detection models with tensor kernel machine.
problem Improving seizure detection accuracy for wearable devices.
method Transfer learning with tensor kernel machine using canonical polyadic decomposition.
result Patient fine-tuned model achieves high performance with smaller model size.
Adaptive sampling detects local concept drift with limited labels.
problem Detecting local concept drift in dynamic environments with scarce labels.
method Combines residual-based exploration and exploitation with EWMA monitoring.
result Superior performance in label efficiency and drift detection accuracy.
EagerNet detects network attacks quickly with less resources.
problem Efficiently detecting network attacks with minimal resources.
method Proposes a new architecture that trades prediction speed for accuracy, evaluating only a subset of layers.
result Comparable accuracies to simple FCNNs achieved with early predictions, saving energy and computational efforts.
We study the problem of detecting critical structures using a graph embedding model. Existing graph embedding models lack the ability to precisely detect critical structures that are specific to a task at the global scale. In this paper, we propose a novel graph embedding model, called the Ego-CNNs, that employs the eg…
RCS-YOLO improves brain tumor detection speed and accuracy.
problem Efficiently detecting brain tumors with high accuracy and speed.
method Proposes RCS-YOLO, a fast and accurate brain tumor detector using YOLO with Reparameterized Convolution and channel Shuffle.
result RCS-YOLO outperforms other YOLO versions in speed and accuracy on brain tumor detection.
Paper uses SDP for community detection with side information.
problem Community detection in graphs with additional non-graph data.
method Formulates SDP relaxation for maximum likelihood node labeling with side information.
result SDP achieves same exact recovery threshold as maximum likelihood with side information.
Tensor networks improve anomaly detection efficiency.
problem Anomaly detection in high-dimensional data.
method Tensor networks for learning a linear transformation over high-dimensional space, penalizing global tendency to normality.
result Tensor networks outperform deep and classical algorithms on various datasets.
PySAD offers a unified Python framework for efficient streaming anomaly detection.
problem Efficient anomaly detection in streaming data with strict constraints.
method Unified architecture with 17+ streaming algorithms, specialized components, and support for multiple learning paradigms.
result PySAD enables real-time processing with bounded memory and is compatible with other Python frameworks.
ECAD detects anomalies without data exchangeability, improving traffic flow detection.
problem Detecting anomalies in spatio-temporal data with missing values.
method ECAD uses conformal prediction to wrap around any regression algorithm, controlling Type-I error without data exchangeability.
result ECAD outperforms other methods in detecting anomalous traffic flow.
FOCuS detects changes in mean from high-frequency data efficiently.
problem Detecting changes in high-frequency data with limited resources.
method FOCuS algorithm that runs multiple window sizes and change sizes simultaneously.
result FOCuS achieves state-of-the-art performance in detecting anomalies.
Efficient algorithm for CLSBM reduces misclassification rate.
problem Reducing misclassification in community detection for CLSBM.
method Spectral-based algorithm for CLSBM, with theoretical misclassification bounds.
result Upper bound on misclassification rate of efficient algorithm.
Paper proposes an efficient method for bounding box annotation in object detection.
problem Manual annotation of bounding boxes is tedious and resource-intensive.
method Iterative training of object detector on small batches of labeled images, with human annotator correcting errors.
result Significant reduction in human annotation effort, up to 75%.
Classifying streaming data requires the development of methods which are computationally efficient and able to cope with changes in the underlying distribution of the stream, a phenomenon known in the literature as concept drift. We propose a new method for detecting concept drift which uses an Exponentially Weighted M…
Survey on LSTM-based anomaly detection for technical systems.
problem Detect anomalies in technical systems due to complex dynamics.
method Use LSTM networks and other AI techniques to detect anomalies considering temporal and contextual characteristics.
result Demonstrates the potential of LSTM networks and graph-based approaches for anomaly detection.
NN-CUSUM detects changes in high-dimensional data using neural networks.
problem Detecting abrupt changes in high-dimensional data.
method Neural network-based CUSUM for online change-point detection.
result NN-CUSUM performs well in detecting changes in high-dimensional data.
BFCR detects anomalies in 1D data efficiently.
problem Anomaly detection in 1D data.
method Braced Fourier Continuation and Regression (BFCR) for efficient anomaly detection.
result BFCR trend lines effectively detect anomalies in 1D data.
New methods detect and create Brunnian links efficiently.
problem Detecting and constructing Brunnian links when each component is known to be an unknot.
method Two practical methods based on observation and handwork.
result Established a general way to construct new Brunnian links in bulk.
A drift detection method for large datasets without labels.
problem Early detection of concept drift in large, unlabeled datasets.
method Classical statistical process control in a label-less setting.
result Better statistical power than previous methods under computational constraints.
Identifying overlapping communities in networks is a challenging task. In this work we present a novel approach to community detection that utilises the Bayesian non-negative matrix factorisation (NMF) model to produce a probabilistic output for node memberships. The scheme has the advantage of computational efficiency…
Efficient online kernel CUSUM detects changes quickly and accurately.
problem Detecting changes in online data streams efficiently.
method Online kernel CUSUM using maximum kernel statistics.
result Increased sensitivity to small changes compared to existing methods.
Spectral method detects communities in sparse hypergraphs, achieving detection threshold.
problem Community detection in sparse hypergraphs.
method Non-backtracking operator and spectral approach.
result Spectral method achieves detection threshold for sparse HSBMs.
A new algorithm detects changes in high-dimensional data efficiently under sampling constraints.
problem Real-time monitoring of high-dimensional streaming data with limited sampling.
method Incorporates multi-armed bandit approaches into sequential change-point detection.
result Proposes TSSRP algorithm for efficient detection of changes.
Deep learning generates efficient change-point detection methods.
problem Detecting change-points in data with various types of change and data behavior.
method Train a neural network to automatically generate detection methods.
result Neural network-based methods are competitive and outperform standard methods in various noise conditions.
Market competition depends on computational complexity, P != NP makes it impossible.
problem Competitive market outcomes require computational intractability.
method Analyzes the computational hardness of collusion detection in markets.
result If P != NP, collusion detection is computationally infeasible, making collusion unstable.
One-Class Boundary Peeling detects outliers efficiently and robustly.
problem Unsupervised outlier detection in diverse data distributions.
method One-Class Boundary Peeling uses flexible boundaries generated by one-class SVMs and iteratively peels them.
result One-Class Boundary Peeling outperforms state-of-the-art methods in synthetic data simulations.
A new method for detecting anomalies in large, high-dimensional data streams using probabilistic forest models.
problem Challenges in detecting anomalies in large, high-dimensional data.
method Probabilistic Mondrian Pólya Forests for summarizing data and estimating underlying probability density.
result State-of-the-art performance with interpretable anomaly scores.
This paper presents the Speech Technology Center (STC) replay attack detection systems proposed for Automatic Speaker Verification Spoofing and Countermeasures Challenge 2017. In this study we focused on comparison of different spoofing detection approaches. These were GMM based methods, high level features extraction …