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 19611970 of 3073 papers

TitleStatusHype
Streaming Machine Learning and Online Active Learning for Automated Visual Inspection0
Structural-Entropy-Based Sample Selection for Efficient and Effective Learning0
Structural query-by-committee0
Structured Prediction via Learning to Search under Bandit Feedback0
Structuring Operative Notes using Active Learning0
Submodularity Cuts and Applications0
Submodularity-Inspired Data Selection for Goal-Oriented Chatbot Training Based on Sentence Embeddings0
Submodular Learning and Covering with Response-Dependent Costs0
Cascade Submodular Maximization: Question Selection and Sequencing in Online Personality Quiz0
Submodular Mutual Information for Targeted Data Subset Selection0
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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