SOTAVerified

Multi-class Classification

Multi-class classification is a type of supervised learning where the goal is to assign an input to one of three or more distinct classes. Unlike binary classification (which has only two classes), multi-class classification handles multiple labels and uses algorithms like logistic regression, decision trees, random forests, SVMs, or neural networks to predict the correct category based on the features of the input data.

Papers

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Showing 726–750 of 903 papers

TitleStatusHype
Semi-supervised Vector-valued Learning: Improved Bounds and AlgorithmsCode0
Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning ModelsCode0
Conformal inference is (almost) free for neural networks trained with early stoppingCode0
Every Untrue Label is Untrue in its Own Way: Controlling Error Type with the Log Bilinear LossCode0
Efficient Robust Optimal Transport with Application to Multi-Label ClassificationCode0
Non-Parametric Calibration for ClassificationCode0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
Efficient Machine Learning Ensemble Methods for Detecting Gravitational Wave Glitches in LIGO Time SeriesCode0
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class AnnealingCode0
Adaptive Gradient Methods Converge Faster with Over-Parameterization (but you should do a line-search)Code0
Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance PropagationCode0
Calibration tests beyond classificationCode0
Characterizing Data Point Vulnerability via Average-Case RobustnessCode0
Leveraging Human-Machine Interactions for Computer Vision Dataset Quality EnhancementCode0
Condensed Gradient BoostingCode0
AppealCase: A Dataset and Benchmark for Civil Case Appeal ScenariosCode0
Tackling Irony Detection using Ensemble ClassifiersCode0
A Semantic Loss Function for Deep Learning with Symbolic KnowledgeCode0
Exponentially Convergent Algorithms for Supervised Matrix FactorizationCode0
Extrapolating Expected Accuracies for Large Multi-Class ProblemsCode0
Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior ShiftCode0
Llama Guard: LLM-based Input-Output Safeguard for Human-AI ConversationsCode0
Reading Between the Leads: Local Lead-Attention Based Classification of Electrocardiogram SignalsCode0
FA-Net: A Fuzzy Attention-aided Deep Neural Network for Pneumonia Detection in Chest X-RaysCode0
Efficient Deep Learning for Stereo MatchingCode0
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All datasetsCOVID-19 CXR DatasetCOVID chest X-rayReuters-52TII-SSRC-23Training and validation dataset of capsule vision 2024 challenge.

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1COVID-CXNetAccuracy (%)94.2—Unverified
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1COVID-ResNetF1 score0.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified