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 2761–2770 of 3073 papers

TitleStatusHype
Active Self-Paced Learning for Cost-Effective and Progressive Face Identification—0
Active Learning and Proofreading for Delineation of Curvilinear Structures—0
Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes—0
Deep Active Learning for Dialogue Generation—0
Knowledge Completion for Generics using Guided Tensor Factorization—0
Active Learning for Speech Recognition: the Power of Gradients—0
Finding Better Active Learners for Faster Literature ReviewsCode0
Factored Contextual Policy Search with Bayesian Optimization—0
Highly Efficient Regression for Scalable Person Re-Identification—0
Active Learning with Oracle Epiphany—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