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 2201–2210 of 3073 papers

TitleStatusHype
A Competitive Algorithm for Agnostic Active Learning—0
A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching—0
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling—0
A Survey on Curriculum Learning—0
A Compression Technique for Analyzing Disagreement-Based Active Learning—0
A Contextual Bandit Approach for Stream-Based Active Learning—0
A critical look at the current train/test split in machine learning—0
ActDroid: An active learning framework for Android malware detection—0
Action State Update Approach to Dialogue Management—0
Active^2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation—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