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 2671–2680 of 3073 papers

TitleStatusHype
Amortized Active Learning for Nonparametric Functions—0
Amortized nonmyopic active search via deep imitation learning—0
Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies from Simulated Nonparametric Functions—0
A Multitask Active Learning Framework for Natural Language Understanding—0
An active learning approach for improving the performance of equilibrium based chemical simulations—0
An Active Learning Approach for Jointly Estimating Worker Performance and Annotation Reliability with Crowdsourced Data—0
An Active Learning Based Approach For Effective Video Annotation And Retrieval—0
An Active Learning-based Approach for Hosting Capacity Analysis in Distribution Systems—0
An Active Learning Framework for Constructing High-fidelity Mobility Maps—0
An Active Learning Framework for Efficient Robust Policy Search—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