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 311–320 of 3073 papers

TitleStatusHype
Active learning of digenic functions with boolean matrix logic programming—0
Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning—0
Gravix: Active Learning for Gravitational Waves Classification Algorithms—0
Unlocking the Power of LLM Uncertainty for Active In-Context Example Selection—0
I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement ParadigmCode1
Robust Offline Active Learning on GraphsCode0
Robust Active Learning (RoAL): Countering Dynamic Adversaries in Active Learning with Elastic Weight Consolidation—0
Reciprocal Learning—0
Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model—0
Hyperbolic Learning with Multimodal Large Language Models—0
Show:102550
← PrevPage 32 of 308Next →

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