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 431–440 of 3073 papers

TitleStatusHype
Uncertainty for Active Learning on Graphs—0
Active Learning with Task Adaptation Pre-training for Speech Emotion RecognitionCode0
A Survey on Deep Active Learning: Recent Advances and New Frontiers—0
Predictive Accuracy-Based Active Learning for Medical Image SegmentationCode0
Computational Job Market Analysis with Natural Language ProcessingCode0
Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagery—0
Making Better Use of Unlabelled Data in Bayesian Active LearningCode1
Annotator-Centric Active Learning for Subjective NLP TasksCode0
Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional DataCode0
Language-Driven Active Learning for Diverse Open-Set 3D Object DetectionCode0
Show:102550
← PrevPage 44 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