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 1041–1050 of 3073 papers

TitleStatusHype
Bayesian Active Learning for Structured Output Design—0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Active Learning with Point Supervision for Cost-Effective Panicle Detection in Cereal Crops—0
Bayesian Active Learning for Multi-Criteria Comparative Judgement in Educational Assessment—0
Active learning with version spaces for object detection—0
Bayesian Active Learning for Discrete Latent Variable Models—0
Efficient Sampling-Based Bayesian Active Learning for synaptic characterization—0
Active Learning with Variational Quantum Circuits for Quantum Process Tomography—0
Active Learning for Domain Classification in a Commercial Spoken Personal Assistant—0
Bayesian active learning for choice models with deep Gaussian processes—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