Region-based classification defends against evasion attacks in DNNs.
problem Adversarial examples can fool DNNs, compromising safety-critical applications.
method Region-based classification using hypercube ensemble for robustness.
result Region-based classification significantly mitigates evasion attacks without sacrificing accuracy.
Art historians and archaeologists have long grappled with the regional classification of ancient Near Eastern ivory carvings. Based on the visual similarity of sculptures, individuals within these fields have proposed object assemblages linked to hypothesized regional production centers. Using quantitative rather than …
New method uses conformalization to create classification regions from ambiguous labels.
problem Creating provable guarantees in classification with uncertain labels.
method Conformal methods applied to credal regions for classification problems.
result New method provides smaller and more disentangled prediction sets.
Enhances DES by removing noise and defining regions more accurately.
problem Incompetent classifier selection in noisy regions and true indecision regions.
method FIRE-DES++ uses equal number of samples from each class and removes noise to define regions more accurately.
result FIRE-DES++ outperforms FIRE-DES and state-of-the-art DES frameworks.
Paper classifies link diagrams on nonorientable surfaces using region crossing changes.
problem Classifying link diagrams on nonorientable surfaces.
method Classification through region crossing changes.
result Classification of link diagrams on nonorientable surfaces.
Meta-learning improves few-shot land cover classification across diverse regions.
problem Capturing diversity in land cover classification across different geographic regions.
method Model-agnostic meta-learning (MAML) algorithm applied to classification and segmentation tasks.
result Few-shot model adaptation outperforms traditional methods in diverse land cover classification tasks.
The paper analyzes deep neural network classification regions and their decision boundaries.
problem Understanding the geometric properties of deep neural network classifiers.
method Empirical investigation of deep neural networks' classification regions and decision boundaries.
result Deep neural networks learn connected classification regions with flat decision boundaries.
The paper develops methods to estimate and assess the risk of binary classification.
problem Estimating the underlying regression function for binary classification.
method Three kernel-based semi-parametric resampling methods are proposed to build confidence regions for the regression function.
result The proposed methods guarantee regions with exact coverage probabilities and are strongly consistent.
The paper develops methods for constructing confidence regions for regression functions in binary classification.
problem Building distribution-free confidence regions for regression functions in binary classification.
method Resampling test and empirical risk minimization approach for model classes with finite pseudo-dimensions and inverse Lipschitz parameterizations.
result Strong uniform consistency and exponential probably approximately correct bounds on the L2 sizes of the regions. Improved dynamic classifier selection by refining regions of competence.
problem Limited performance of dynamic selection systems due to noisy regions.
method Integrates a filter and an adaptive distance to enhance regions of competence.
result Significant increase in recognition performance and decrease in computational cost.
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.
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.
Heavy-tailed distributions are frequently used to enhance the robustness of regression and classification methods to outliers in output space. Often, however, we are confronted with "outliers" in input space, which are isolated observations in sparsely populated regions. We show that heavy-tailed stochastic processes (…
Proposes KNORA-B and KNORA-BI for DES, improving classification performance.
problem Selecting locally competent classifiers for new test samples.
method KNORA-B and KNORA-BI use nearest neighbors to reduce region of competence, maintaining at least one sample from each class.
result KNORA-BI outperforms state-of-the-art techniques on imbalance datasets.
Mapper-GIN simplifies 3D point cloud classification with lightweight structure.
problem Robust 3D point cloud classification under corruption.
method Mapper algorithm for structural decomposition, GIN for graph classification.
result Mapper-GIN achieves competitive accuracy with minimal parameters.
This paper proposes an improved active learning method using classification trees.
problem Reducing the size of training sets while maintaining high accuracy in supervised learning.
method A wrapper active learning method using a classification tree to sub-sample from low-entropy regions.
result The proposed method constructs accurate classification models even with severely restricted labeled data.
Graph Attention Networks improve image classification with superpixels.
problem Classifying images with irregular shapes and edges.
method Transform images into superpixel graphs, then apply GATs.
result GATs outperform other GNN models in image classification.
Searchlight factor model identifies shared fMRI information in brain regions.
problem Inherent anatomical and functional variability across subjects in multi-subject fMRI analysis.
method Shared response factor model with searchlight approach to pinpoint shared information in small contiguous regions.
result The searchlight approach can identify and pinpoint informative local regions of shared fMRI information.
Novel graph-based framework for hyperspectral image classification using superpixels.
problem High classification accuracy with limited labelled data in hyperspectral images.
method Superpixel method for defining local regions, spectral and spatial features extraction, contracted graph representation, semi-supervised classifier.
result Our approach produces accurate classifications with minimal labelled data, outperforming state-of-the-art techniques.
Develops probabilistic safety regions for scalable classifiers.
problem Minimizing misclassification errors in supervised classification.
method Introduces probabilistic safety regions and scalable classifiers.
result Probabilistic certifications for classifier performance.
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.
New methods improve graph classification accuracy using regional uncertainty.
problem Improving graph classification accuracy without labeled data.
method Proposed two new selection criteria for active learning: regional uncertainty and extended page-rank.
result Extended page-rank outperforms other methods when the fraction of labeled nodes is low.
MPSA-DenseNet improves accent classification accuracy.
problem Accurate English accent identification.
method Combines multi-task learning and PSA attention mechanism with DenseNet.
result MPSA-DenseNet outperforms other models in accent classification.
Divides state space into regions with identical term structure shapes.
problem Classifying term structure shapes in the two-factor Vasicek model.
method Using envelopes and winding numbers to divide and classify the state space.
result Nearly complete classification of parameter space regarding term structure shapes.
In this paper an approach based on expectation maximization (EM) clustering to find the climate regions and a support vector machine to build a predictive model for each of these regions is proposed. To minimize the biases in the estimations a ten cross fold validation is adopted both for obtaining clusters and buildin…
A new method classifies hyperspectral images using dynamic graph convolutional networks.
problem Complex spatial context in HSI classification leads to inaccurate results.
method Develops a GCN-based method that captures long-range contextual relations and refines graph edges.
result Significant improvement in HSI classification performance compared to state-of-the-art methods.
Algorithm identifies controversial regions in classifier disagreements.
problem Understanding regions of high disagreement among classifiers.
method Exceptional Model Mining framework.
result Shows usefulness in identifying new phenomena in well-explored datasets.
Develops counterfactual visual explanations to show how images could change to classify differently.
problem Creating understandable explanations for vision system predictions.
method Selects a distractor image and identifies spatial regions to modify for different classification.
result Users trained with counterfactual explanations perform better in fine-grained bird classification.
Reweighting improves risk bounds in certain data regions.
problem Improving risk bounds in classification and heteroscedastic regression.
method Weighted empirical risk minimization with a data-dependent weight function.
result A weighted ERM estimator can achieve superior performance in specific sub-regions.
Developed a multiway classification method for sparse data.
problem Classification of multiway arrays with sparsity.
method Extended Distance Weighted Discrimination (DWD) to multiway context, accounting for sparsity.
result Improves classification accuracy in multiway structured data.
LIME outperforms other explainers in identifying adversarial attack regions.
problem Evaluating explainers for detecting adversarial attacks in neural networks.
method Quantitative and qualitative investigation of three explainers on adversarial examples.
result LIME outperforms classic salience and guided backpropagation in identifying adversarial attack regions.
The study examines the properties of linear regions in DNNs and how optimization techniques affect them.
problem Understanding the expressivity of deep neural networks through their linear regions.
method Empirical analysis of local properties of linear regions, including inspheres, hyperplane directions, decision boundaries, and surrounding regions.
result Different optimization techniques lead to distinct linear regions, even with similar classification accuracy.
CNNs improve InSAR coherence classification.
problem Improving demarcation of InSAR imagery regions based on coherence.
method Convolutional Neural Networks (CNNs) for preprocessing and classification.
result CNNs reduce misclassifications in incoherent regions and outperform established methods.
Paper uses t-SNE to classify China's Internet finance risks.
problem Systemic risk characteristics of China's Internet finance during macroeconomic shocks.
method t-SNE machine learning algorithm for data mining and risk classification.
result Identified peak and thick-tail characteristics of Internet financial systemic risk.
The paper proposes a method to focus on discriminative regions for better unsupervised domain adaptation.
problem Unsupervised domain adaptation with limited target domain labels.
method Probabilistic certainty estimate of regions to focus on during classification.
result State-of-the-art results on various datasets compared to recent methods.
RAU integrates attention into GRU for better sequence learning.
problem Lack of attention mechanism in GRU leads to information redundancy or loss.
method RAU adds an attention gate to GRU to adaptively focus on regions of interest.
result RAU consistently outperforms GRU and other methods in various tasks.
Study uses machine learning to classify autism based on brain connectivity variability.
problem Classifying autism using brain functional connectivity.
method Machine learning models trained on brain imaging data from ABIDE database.
result Increased FC variability in brain regions associated with low variability in ASD patients.
This paper presents a new semi-supervised framework with convolutional neural networks (CNNs) for text categorization. Unlike the previous approaches that rely on word embeddings, our method learns embeddings of small text regions from unlabeled data for integration into a supervised CNN. The proposed scheme for embedd…
We show the existence of isometric (or Ford) fundamental regions for a large class of subgroups of the isometry group of any rank one Riemannian symmetric space of noncompact type. The proof does not use the classification of symmetric spaces. All hitherto known existence results of isometric fundamental regions and do…
SOCP uses SOM to find groups and local calibration buffers for better regional coverage.
problem Heterogeneous regional coverage gaps in conformal prediction.
method Self-Organizing Map (SOM) for group discovery; local calibration buffers at BMU or fixed grid.
result Reduces regional coverage gaps on 7/8 benchmarks by 7.1%.
New insights into how neural networks classify data.
problem Understanding the topological structure of decision regions in ReLU networks.
method Defining generic and transversal ReLU networks, and using linear complexes to identify obstructions.
result Generic, transversal ReLU networks have at most one bounded connected component in their decision regions.
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.
Efficient system classifies EEG signals for cognitive tasks using nuclear features.
problem Classification of raw EEG signals for cognitive tasks is challenging.
method Singular value decomposition for computing dominant variances of EEG signals, using them as nuclear features, and a simple classifier.
result Nuclear features from frontal brain region achieved 100% prediction accuracy.
MDGCN improves hyperspectral image classification by dynamically updating graphs.
problem Traditional CNNs struggle with irregular image regions and class boundaries.
method MDGCN uses dynamic graph convolution on hyperspectral images, adapting to local regions.
result MDGCN outperforms state-of-the-art methods on benchmark datasets.
SVM predicts regional rainfall with varying accuracy, best in central US.
problem Regional rainfall prediction for social and economic impact planning.
method Support Vector Machine (SVM) applied to sequences of daily rainfall maps.
result SVM predictions for central region outperform untrained classifier.
Local probabilistic models simplify Bayesian classification for complex data.
problem Complex real-world data requires simpler models than global ones.
method Establish local probabilistic models for local regions, relaxing global assumptions.
result Local probabilistic models improve classification accuracy on real-world datasets.
Donor-aware scRNA-seq benchmarks improve classification accuracy in inflammatory bowel disease.
problem Influenza disease classification from scRNA-seq data is prone to donor-level confounding.
method Developed and evaluated three feature representations across two IBD cohorts.
result Compartment-stratified CLR composition and GatedStructuralCFN embeddings outperform linear models in classification accuracy.
Geotagged data can be used to describe regions in the world and discover local themes. However, not all data produced within a region is necessarily specifically descriptive of that area. To surface the content that is characteristic for a region, we present the geographical hierarchy model (GHM), a probabilistic model…