SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Showing 30613070 of 3073 papers

TitleStatusHype
Graudally Applying Weakly Supervised and Active Learning for Mass Detection in Breast Ultrasound ImagesCode0
Towards a relation extraction framework for cyber-security conceptsCode0
Greed is Good: Exploration and Exploitation Trade-offs in Bayesian OptimisationCode0
Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource SettingsCode0
Active Learning Framework for Cost-Effective TCR-Epitope Binding Affinity PredictionCode0
Beyond Grids: Multi-objective Bayesian Optimization With Adaptive DiscretizationCode0
TAR on Social Media: A Framework for Online Content ModerationCode0
Task-Aware Active Learning for Endoscopic Image AnalysisCode0
Weighted Sparse Subspace Representation: A Unified Framework for Subspace Clustering, Constrained Clustering, and Active LearningCode0
Revisiting Sample Size Determination in Natural Language UnderstandingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified