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 1481–1490 of 3073 papers

TitleStatusHype
Enhanced sampling of robust molecular datasets with uncertainty-based collective variables—0
Enhancing Active Learning for Sentinel 2 Imagery through Contrastive Learning and Uncertainty Estimation—0
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models—0
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders—0
Enhancing Modality Representation and Alignment for Multimodal Cold-start Active Learning—0
Enhancing personalised thermal comfort models with Active Learning for improved HVAC controls—0
An active learning approach for improving the performance of equilibrium based chemical simulations—0
Discrepancy-based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images—0
A Multitask Active Learning Framework for Natural Language Understanding—0
Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space—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