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 15311540 of 3073 papers

TitleStatusHype
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
Subsequence Based Deep Active Learning for Named Entity Recognition0
Subspace Clustering with Active Learning0
Subspace-Distance-Enabled Active Learning for Efficient Data-Driven Model Reduction of Parametric Dynamical Systems0
Sufficient Conditions for Agnostic Active Learnable0
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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