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 1711–1720 of 3073 papers

TitleStatusHype
Photonic architecture for reinforcement learning—0
Phrase-level Active Learning for Neural Machine Translation—0
Physics-constrained Active Learning for Soil Moisture Estimation and Optimal Sensor Placement—0
Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systems—0
Physics-enhanced deep surrogates for partial differential equations—0
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence—0
Physics-informed EDFA Gain Model Based on Active Learning—0
Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design—0
Physics-Information-Aided Kriging: Constructing Covariance Functions using Stochastic Simulation Models—0
Picking groups instead of samples: A close look at Static Pool-based Meta-Active 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