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 2871–2880 of 3073 papers

TitleStatusHype
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning—0
Bayesian Semisupervised Learning with Deep Generative Models—0
BayesOpt: A Library for Bayesian optimization with Robotics Applications—0
Bayes-Optimal Entropy Pursuit for Active Choice-Based Preference Learning—0
Beating the Minimax Rate of Active Learning with Prior Knowledge—0
BenchDirect: A Directed Language Model for Compiler Benchmarks—0
Benchmarking Active Learning for NILM—0
Benchmarking Active Learning Strategies for Materials Optimization and Discovery—0
Benchmarking Multi-Domain Active Learning on Image Classification—0
Benchmarks and Algorithms for Offline Preference-Based Reward Learning—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