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 1191–1200 of 3073 papers

TitleStatusHype
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget—0
Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition—0
Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection—0
Binary Classification with XOR Queries: Fundamental Limits and An Efficient Algorithm—0
Crowd Sourcing based Active Learning Approach for Parking Sign Recognition—0
Crowdsourcing Complex Language Resources: Playing to Annotate Dependency Syntax—0
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation—0
Crown-Like Structures in Breast Adipose Tissue: Finding a 'Needle-in-a-Haystack' using Artificial Intelligence and Collaborative Active Learning on the Web—0
Accelerating engineering design by automatic selection of simulation cases through Pool-Based Active Learning—0
Deciding when to stop: Efficient stopping of active learning guided drug-target prediction—0
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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