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 551–560 of 3073 papers

TitleStatusHype
Railway LiDAR semantic segmentation based on intelligent semi-automated data annotation—0
AutoAL: Automated Active Learning with Differentiable Query Strategy SearchCode0
A Simplifying and Learnable Graph Convolutional Attention Network for Unsupervised Knowledge Graphs Alignment—0
An Active Learning Framework for Inclusive Generation by Large Language Models—0
MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active LearningCode0
Active Learning for Robust and Representative LLM Generation in Safety-Critical Scenarios—0
ALVIN: Active Learning Via INterpolation—0
A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences—0
MelissaDL x Breed: Towards Data-Efficient On-line Supervised Training of Multi-parametric Surrogates with Active Learning—0
Improved detection of discarded fish species through BoxAL active learningCode0
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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