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 19311940 of 3073 papers

TitleStatusHype
Sophisticated Inference0
SoQal: Selective Oracle Questioning in Active Learning0
Sparse Semi-Supervised Action Recognition with Active Learning0
Spatially regularized active diffusion learning for high-dimensional images0
Speeding Up BatchBALD: A k-BALD Family of Approximations for Active Learning0
SpiroActive: Active Learning for Efficient Data Acquisition for Spirometry0
Sprucing up the trees -- Error detection in treebanks0
SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions0
STAND: Data-Efficient and Self-Aware Precondition Induction for Interactive Task Learning0
STARdom: an architecture for trusted and secure human-centered manufacturing systems0
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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