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 29812990 of 3073 papers

TitleStatusHype
Confidence Estimation for Object Detection in Document Images0
Confident Coreset for Active Learning in Medical Image Analysis0
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion0
Congruence-based Learning of Probabilistic Deterministic Finite Automata0
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget0
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost0
Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification0
Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions0
Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks0
Constrained Bayesian Optimization with Adaptive Active Learning of Unknown Constraints0
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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