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 2461–2470 of 3073 papers

TitleStatusHype
Active Learning of Piecewise Gaussian Process Surrogates—0
Active Learning of Quantum System Hamiltonians yields Query Advantage—0
Active Learning of Sequential Transducers with Side Information about the Domain—0
Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations—0
Active Learning of SVDD Hyperparameter Values—0
Active learning of the thermodynamics-dynamics tradeoff in protein condensates—0
Active learning of timed automata with unobservable resets—0
Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation—0
Active Learning on a Programmable Photonic Quantum Processor—0
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation—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