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

Showing 281290 of 3073 papers

TitleStatusHype
Diversify and Conquer: Diversity-Centric Data Selection with Iterative RefinementCode1
MALADY: Multiclass Active Learning with Auction Dynamics on Graphs0
Active Learning to Guide Labeling Efforts for Question Difficulty EstimationCode0
Crown-Like Structures in Breast Adipose Tissue: Finding a 'Needle-in-a-Haystack' using Artificial Intelligence and Collaborative Active Learning on the Web0
DEMAU: Decompose, Explore, Model and Analyse Uncertainties0
Automated Discovery of Pairwise Interactions from Unstructured Data0
STAND: Data-Efficient and Self-Aware Precondition Induction for Interactive Task Learning0
A Scalable Algorithm for Active Learning0
FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression0
Bounds on the Generalization Error in Active Learning0
Show:102550
← PrevPage 29 of 308Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified