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 1381–1390 of 3073 papers

TitleStatusHype
Distribution-Dependent Sample Complexity of Large Margin Learning—0
An Active Learning Framework for Efficient Robust Policy Search—0
Computer-assisted Speaker Diarization: How to Evaluate Human Corrections—0
Diverse mini-batch Active Learning—0
Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization—0
An active learning framework for multi-group mean estimation—0
Active Learning for New Domains in Natural Language Understanding—0
AcTune: Uncertainty-Aware Active Self-Training for Active Fine-Tuning of Pretrained Language Models—0
An Active Learning Framework with a Class Balancing Strategy for Time Series Classification—0
DutchSemCor: Targeting the ideal sense-tagged corpus—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