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 1401–1410 of 3073 papers

TitleStatusHype
Minority Class Oriented Active Learning for Imbalanced Datasets—0
Active Learning Over Multiple Domains in Natural Language Tasks—0
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning—0
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times—0
Towards Robust Deep Active Learning for Scientific Computing—0
Dominant Set-based Active Learning for Text Classification and its Application to Online Social Media—0
Approximate Bayesian Computation with Domain Expert in the LoopCode0
TrustAL: Trustworthy Active Learning using Knowledge Distillation—0
Competition over data: how does data purchase affect users?—0
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images—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