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 561–570 of 3073 papers

TitleStatusHype
Interactive Event Sifting using Bayesian Graph Neural Networks—0
Language Model-Driven Data Pruning Enables Efficient Active Learning—0
STONE: A Submodular Optimization Framework for Active 3D Object DetectionCode0
Structural-Entropy-Based Sample Selection for Efficient and Effective Learning—0
GPT-4o as the Gold Standard: A Scalable and General Purpose Approach to Filter Language Model Pretraining Data—0
Dual Active Learning for Reinforcement Learning from Human Feedback—0
Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes—0
Provably Accurate Shapley Value Estimation via Leverage Score Sampling—0
Differentially Private Active Learning: Balancing Effective Data Selection and PrivacyCode0
Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop TrainingCode0
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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