Detect anomalous regions in spatio-temporal data.
problem Detecting coherent anomalous regions in large spatio-temporal datasets.
method Maximally Divergent Intervals (MDI) framework for unbiased Kullback-Leibler divergence.
result Our method identifies coherent anomalous regions in various data types.
OneFlow detects anomalies by finding a minimal volume region, outperforming other methods.
problem Anomaly detection in data with complex outlier structures.
method Flow-based one-class classifier that uses a minimal volume region to define outliers.
result OneFlow outperforms other methods in real-world anomaly detection tasks.
New method detects and locates changes in spatio-temporal point processes.
problem Detecting and localizing changes in spatio-temporal data.
method Score-based, likelihood-free approach estimating change time and region.
result The method provides theoretical guarantees on detection and localization accuracy.
A framework uses complex networks for image segmentation.
problem Over-segmentation in image segmentation.
method Initial segmentation, adaptive network construction, community detection.
result The proposed framework improves segmentation performance.
Region detection in Gaussian Markov fields with limited samples.
problem Consistent graph recovery in sample deficient scenarios.
method Partitioning the graph into spatial regions with similar edge parameters and regular boundaries, developing new sample complexity bounds, and introducing an efficient region growing algorithm.
result A bounded number of samples can be sufficient for consistent region recovery.
A new method detects interactions in machine learning models.
problem Interpreting non-linear and interaction effects in machine learning models.
method Regional effect plots with implicit interaction detection.
result The method quantifies and interprets feature effects reliably, less confounded by interactions.
Hidden Markov Models detect hand gestures from wearable sEMG signals.
problem Detecting activity regions in continuous sEMG signals.
method Hidden Markov Models applied to sEMG signals for gesture recognition.
result Average accuracy of 96.25% for activity onsets and 87.5% for activity terminations.
AUCRSS detects change points in partially observed multivariate autocorrelated data.
problem Detecting change points in multivariate autocorrelated data with limited sensing resources.
method Adaptive Upper Confidence Region (AUCRSS) with state space model (SSM), adaptive sampling policy, and generalized likelihood ratio test.
result The method outperforms existing approaches in detecting change points efficiently.
Improved object detection for scientific document images.
problem Current object detectors fail to accurately localize regions in scientific document images.
method Revised R-CNN model with region embedding for fine-grained proposals.
result 17% mAP improvement over standard object detection models.
Sharp boundaries for detecting dense subhypergraphs established.
problem Detecting dense subhypergraphs in random hypergraphs.
method Established sharp detection boundaries for known and unknown edge probabilities.
result Sharp detectable regions differ significantly from graph counterparts.
Paper develops SKPD framework for signal region detection in image regression.
problem Limited research on image region detection in high-resolution image regression.
method Sparse Kronecker Product Decomposition (SKPD) framework for matrices and tensors.
result Computed solutions converge to truth with guaranteed consistency.
ANODE uses neural density estimation for anomaly detection in physics.
problem Detecting localized anomalies in signal regions with limited background information.
method Estimate data and background densities, construct likelihood ratio, and enhance significance.
result ANODE enhances dijet bump hunt significance by up to 7x with 10% background accuracy.
A new VAD method uses respiration patterns from video to detect speech.
problem Improving VAD performance in noisy audio recordings.
method Extract respiration patterns from video, use neural models to detect speech.
result Efficacy demonstrated through experiments on real acoustic environments.
Paper develops a diagnostic test for detecting convergence in SGD with constant step size.
problem Detecting convergence in stochastic gradient descent with constant step size.
method Statistical diagnostic test to detect phase transition in convergence.
result The diagnostic region coincides with the convergence region for a class of loss functions.
We introduce a novel approach for parallelizing MCMC inference in models with spatially determined conditional independence relationships, for which existing techniques exploiting graphical model structure are not applicable. Our approach is motivated by a model of seismic events and signals, where events detected in d…
We present an approach for polarimetric Synthetic Aperture Radar (SAR) image region boundary detection based on the use of B-Spline active contours and a new model for polarimetric SAR data: the GHP distribution. In order to detect the boundary of a region, initial B-Spline curves are specified, either automatically or…
Proposes a method to estimate acceptance regions for many classes, including new ones.
problem Lack of methods to handle new classes in set-valued classification.
method Generalized Prediction Set (GPS) approach to estimate acceptance regions.
result Achieves a good balance between accuracy, efficiency, and anomaly detection.
Framework detects and classifies multi-label RBC images from microscopic images.
problem Challenges in separating touching or overlapping cells for classification.
method Region proposal model + CNN feature extraction + multi-label prediction networks.
result Framework achieves good performance in automatic cell detection and classification.
We show linear XOR classification is possible and propose equality separation for anomaly detection.
problem Linearly separating XOR data.
method Equality separation, adapting SVM objective for data within/outside margin.
result Equality separation can detect both seen and unseen anomalies.
A saliency detection method for ECT images segments cellular components without supervision.
problem Automatic segmentation of cellular components from ECT images is difficult due to structural complexity and imaging limits.
method Supervoxel over-segmentation, feature extraction, feature matrix decomposition, and computation of saliency.
result The method successfully labels most salient regions detected by a human observer and filters out background regions.
Proposes a continuous scan statistic to detect anomalies more accurately.
problem Binary region definitions limit the detection of smooth anomalies.
method Kernel Spatial Scan Statistic (KSS) that allows continuous point contributions.
result Efficient computation and high statistical power for detecting anomalous regions.
Study identifies high-density anomalies in normal data regions.
problem Detecting anomalies in normal data regions.
method Introduces non-parametric algorithmic frameworks for unsupervised detection.
result IPP framework yields the best detection results.
Study information limits for community detection in sub-hypergraphs.
problem Identify limits for exact community detection in sub-hypergraphs.
method Use Fano's inequality to define model parameters and identify success and failure regions.
result Identify regions where algorithms succeed or fail in exact recovery.
RGI improves robustness of GAN-inversion for image restoration and anomaly detection.
problem Robustness of GAN-inversion to unknown gross corruptions.
method Proposes RGI and R-RGI methods with provable robustness guarantees.
result Restored images and corrupted region masks converge to ground truth under mild assumptions.
New algorithm detects anomalies by forcing samples to displace mass in low-density regions.
problem Detecting anomalies in datasets.
method Mass Repulsing Optimal Transport (MROT) approach.
result Our algorithm improves anomaly detection over existing methods.
Deep object detection improves mitotic nucleus detection in breast cancer biopsies.
problem Challenges in automated mitotic nucleus detection in breast cancer histopathological images.
method Adapted Mask R-CNN for deep object detection, initially selects candidate regions with maximum recall, refines them with multi-object loss function.
result Improved discrimination ability (F-score of 0.86) and significant precision (0.86) for mitotic nuclei compared to two-stage models.
New CNN architecture detects face spoofing with deep local features.
problem Face recognition systems are vulnerable to face spoofing attacks.
method Two-step CNN architecture: first learns features from facial regions, then fine-tunes on whole images.
result Improves face spoofing detection performance and convergence speed.
A new algorithm detects changepoints in labeled and unlabeled data.
problem Accurate detection of abrupt changes in partially labeled data.
method Labeled Optimal Partitioning (LOPART) algorithm that fits train labels and predicts unlabeled changepoints.
result LOPART provides more accurate predictions than existing methods in both train and test sets.
Paper proposes a new method to automatically select Gaussian kernel bandwidth for SVDD.
problem Selecting optimal Gaussian kernel bandwidth for SVDD is crucial but challenging.
method Automatic unsupervised method for selecting Gaussian kernel bandwidth.
result The selected bandwidth is competitive with existing methods and can be computed quickly.
We describe explicitly the large volume isoperimetric regions of a natural class of asymptotically flat manifolds, in any dimension. These isoperimetric regions detect the mass and the center of mass of such manifolds when viewed as initial data sets for the Einstein equations in general relativity. Using the positivit…
Method predicts brain regions for neuroimaging phenotypes.
problem Predicting phenotypes from brain networks without static community structure.
method Supervised community detection using block-structured regularization and ADMM optimization.
result The method identifies task-specific brain regions that improve phenotype prediction.
A multi-stage reinforcement learning method for object detection.
problem Efficiently detecting objects within images with high accuracy.
method Hierarchical tree-like region candidates, zoom and refinement stages, aspect ratio modification, multiple reward metrics.
result The multi-stage approach leads to more correct detections compared to single-stage methods.
Develops deep neural networks for accurate seizure detection.
problem Seizure detection using EEG signals and brain connectivity.
method Combines EBC, DMNN, MENN, and MFNN methods.
result DMNN achieves highest accuracy (99.43%).
Anomaly detection scores from VAE gradients improve tumor detection.
problem Improving anomaly detection in medical imaging.
method Using Variational Autoencoders to approximate anomaly ratings.
result Variance Autoencoder gradient-based ratings outperform other methods in tumor detection.
AKM2D framework speeds up anomaly detection in point-based sensing.
problem Efficient anomaly detection in point-based sensing systems.
method Adaptive Kernelized Maximum-Minimum Distance (AKM2D) framework for intelligent sequential sampling. result Balances exploration and exploitation for accurate anomaly quantification.
Multivariate Pattern (MVP) classification holds enormous potential for decoding visual stimuli in the human brain by employing task-based fMRI data sets. There is a wide range of challenges in the MVP techniques, i.e. decreasing noise and sparsity, defining effective regions of interest (ROIs), visualizing results, and…
Graph neural networks detect collusion patterns across markets.
problem Detecting and predicting collusion in different national markets.
method Two-phase approach using GNNs for zero-shot learning and OOD generalization.
result GNNs outperform NNs in detecting complex collusive patterns.
Graph embedding improves fMRI classification and reveals brain region differences in ASD.
problem Difficult to embed informative brain fMRI representations due to high dimensionality and low SNR.
method Modelled fMRI as a graph, used GNN to learn from graph data, incorporated mutual information loss (Infomax).
result Infomax graph embedding improves classification performance and reveals separable nodal representations of ASD and HC groups.
The paper analyzes convergence in SGD with momentum and proposes a diagnostic test.
problem Detecting convergence in stochastic gradient descent with momentum.
method Analyzes the transient and stationary phases of SGD with momentum, constructs a statistical diagnostic test.
result The proposed diagnostic test effectively detects convergence in the stationary phase of SGD with momentum.
Geometrically, high-likelihood regions in DGMs are unlikely to generate OOD data.
problem The paradox of high-likelihood OOD detection in deep generative models.
method Local intrinsic dimension estimation to identify high-likelihood regions that do not generate OOD data.
result A method pairing likelihoods and LID estimates for reliable OOD detection.
Effective nonparametric anomaly detection for high-dimensional data.
problem Timely detection of abrupt anomalies in high-dimensional data.
method Nonparametric algorithms using univariate summary statistics and submanifold learning.
result Asymptotic guarantees for accurate anomaly detection.
Study detects boundaries in unlabeled noisy images without labels.
problem Detecting boundaries in unlabeled noisy images without labels.
method Proposed a continuous hinge-type surrogate loss for boundary detection, combined with deep neural networks.
result Deep neural network achieves minimax-optimal boundary recovery rate under piecewise smooth boundary model.
Method detects new physics signals without prior knowledge.
problem Selecting signal regions for novel particles.
method Model-agnostic approach using low-pass filtering and density estimation.
result Efficiently identifies data-driven signal regions in high-dimensional feature space.
FLOPART solves peak detection by creating accurate train and test set predictions.
problem Correctly detecting peaks in sequential data.
method Dynamic programming changepoint algorithm with zero train label errors.
result FLOPART provides highly accurate predictions on both train and test sets.
Input-cell attention improves RNN saliency detection over time.
problem Vanishing saliency in RNNs makes them unreliable for detecting important features at arbitrary time intervals.
method Proposes input-cell attention, a novel RNN cell structure that uses a fixed-size matrix embedding to attend to different inputs from current or previous time steps.
result Input-cell attention produces a saliency map that can detect important features regardless of their occurrence in time.
General predictive models do not provide a measure of confidence in predictions without Bayesian assumptions. A way to circumvent potential restrictions is to use conformal methods for constructing non-parametric confidence regions, that offer guarantees regarding validity. In this paper we provide a detailed descripti…
Associating image regions with text queries has been recently explored as a new way to bridge visual and linguistic representations. A few pioneering approaches have been proposed based on recurrent neural language models trained generatively (e.g., generating captions), but achieving somewhat limited localization accu…
Detects and mitigates rare subclasses in deep neural networks.
problem Underrepresented classes in training datasets reduce classifier performance.
method Commonality metric for detection, methods for reducing impact during training and exploitation.
result Models compensate for rare subclasses, improving performance and runtime identification.