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

Showing 29812990 of 3073 papers

TitleStatusHype
Optimal Bayesian Affine Estimator and Active Learning for the Wiener ModelCode0
Active Learning Design: Modeling Force Output for Axisymmetric Soft Pneumatic ActuatorsCode0
Regional based query in graph active learningCode0
Automated Seed Quality Testing System using GAN & Active LearningCode0
Toward Optimal Probabilistic Active Learning Using a Bayesian ApproachCode0
Active learning in annotating micro-blogs dealing with e-reputationCode0
Active Learning-Based Species Range EstimationCode0
Low Rank Learning for Offline Query OptimizationCode0
Active Learning amidst Logical KnowledgeCode0
From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified