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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1151–1160 of 3073 papers

TitleStatusHype
A Two-Stage Active Learning Algorithm for k-Nearest Neighbors—0
Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance—0
Active learning using weakly supervised signals for quality inspection—0
A Transfer Learning Based Active Learning Framework for Brain Tumor Classification—0
Active Learning for Coreference Resolution—0
Active Discovery of Network Roles for Predicting the Classes of Network Nodes—0
A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree—0
Active learning using region-based sampling—0
A System for Generating Multiple Choice Questions: With a Novel Approach for Sentence Selection—0
Information Losses in Neural Classifiers from Sampling—0
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Benchmark Results

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