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 1881–1890 of 3073 papers

TitleStatusHype
Selective Sampling with Drift—0
SelfAct: Personalized Activity Recognition based on Self-Supervised and Active Learning—0
Self-consistent Validation for Machine Learning Electronic Structure—0
Self-Correcting Bayesian Optimization through Bayesian Active Learning—0
Self-driving lab discovers principles for steering spontaneous emission—0
Self-Excitation: An Enabler for Online Thermal Estimation and Model Predictive Control of Buildings—0
Self-learning Emulators and Eigenvector Continuation—0
Self-supervised self-supervision by combining deep learning and probabilistic logic—0
Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluation—0
Semantic Parsing in Limited Resource Conditions—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