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 391–400 of 3073 papers

TitleStatusHype
Generative Active Learning for Long-tailed Instance SegmentationCode2
Effective Data Selection for Seismic Interpretation through Disagreement—0
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders—0
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model SelectionCode0
Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images—0
A Survey of Latent Factor Models in Recommender Systems—0
A Data-Centric Framework for Machine Listening Projects: Addressing Large-Scale Data Acquisition and Labeling through Active Learning—0
Towards Efficient Disaster Response via Cost-effective Unbiased Class Rate Estimation through Neyman Allocation Stratified Sampling Active Learning—0
Salutary Labeling with Zero Human Annotation—0
Entity Alignment with Noisy Annotations from Large Language ModelsCode0
Show:102550
← PrevPage 40 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