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 371–380 of 3073 papers

TitleStatusHype
Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center DatasetCode1
Towards Bayesian Data Selection—0
SS-ADA: A Semi-Supervised Active Domain Adaptation Framework for Semantic SegmentationCode1
Enhancing Text Classification through LLM-Driven Active Learning and Human AnnotationCode0
Active search for Bifurcations—0
Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification—0
Understanding active learning of molecular docking and its applicationsCode1
Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval—0
Deep Bayesian Active Learning for Preference Modeling in Large Language ModelsCode0
Online Bandit Learning with Offline Preference Data for Improved RLHF—0
Show:102550
← PrevPage 38 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