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 1261–1270 of 3073 papers

TitleStatusHype
Active Learning for Automated Visual Inspection of Manufactured Products—0
Active Deep Kernel Learning of Molecular Functionalities: Realizing Dynamic Structural Embeddings—0
A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching—0
A Bayesian Active Learning Approach to Comparative Judgement—0
Bayesian Active Learning for Semantic Segmentation—0
Active Learning for Binary Classification with Abstention—0
Applying LLMs to Active Learning: Towards Cost-Efficient Cross-Task Text Classification without Manually Labeled Data—0
Active Learning of Ordinal Embeddings: A User Study on Football Data—0
Applied metamodelling for ATM performance simulations—0
Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study—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