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 961–970 of 3073 papers

TitleStatusHype
Benchmarking Active Learning Strategies for Materials Optimization and Discovery—0
Benchmarking Multi-Domain Active Learning on Image Classification—0
Bayesian Semisupervised Learning with Deep Generative Models—0
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning—0
Benchmarks and Algorithms for Offline Preference-Based Reward Learning—0
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure—0
ActiveMatch: End-to-end Semi-supervised Active Representation Learning—0
Best Arm Identification for Contaminated Bandits—0
Active Few-Shot Fine-Tuning—0
Bayesian Pool-based Active Learning With Abstention Feedbacks—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