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 1921–1930 of 3073 papers

TitleStatusHype
Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions—0
Active Learning from Crowd in Document Screening—0
Uncertainty estimation for molecular dynamics and samplingCode1
ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference LandscapesCode0
LADA: Look-Ahead Data Acquisition via Augmentation for Active Learning—0
Action State Update Approach to Dialogue Management—0
Human-Like Active Learning: Machines Simulating the Human Learning Process—0
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning—0
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?Code0
Deep Active Learning with Augmentation-based Consistency EstimationCode0
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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