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 2451–2460 of 3073 papers

TitleStatusHype
Active Learning for One-Class Classification Using Two One-Class Classifiers—0
Guess What's on my Screen? Clustering Smartphone Screenshots with Active Learning—0
Risk-Aware Active Inverse Reinforcement LearningCode0
Efforts estimation of doctors annotating medical image—0
Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision—0
Active Learning with TensorBoard Projector—0
An Active Learning Framework for Efficient Robust Policy Search—0
Weakly Supervised Active Learning with Cluster Annotation—0
A General Approach to Domain Adaptation with Applications in Astronomy—0
Active Learning and CSI Acquisition for mmWave Initial Alignment—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