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 571–580 of 3073 papers

TitleStatusHype
Active Learning of Convex Halfspaces on Graphs—0
Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-based Multiple Instance Learning—0
Active Learning of Continuous-time Bayesian Networks through Interventions—0
Active Learning of Classifiers with Label and Seed Queries—0
Active Learning-Enhanced Dual Control for Angle-Only Initial Relative Orbit Determination—0
Active Curriculum Learning—0
Active Learning of Causal Structures with Deep Reinforcement Learning—0
Active learning of causal probability trees—0
Active Learning Enables Extrapolation in Molecular Generative Models—0
Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy—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