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 27012710 of 3073 papers

TitleStatusHype
A Survey on Multi-Task LearningCode0
Deep Active Learning for Named Entity RecognitionCode1
On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL0
Predicting the Quality of Short Narratives from Social Media0
A Tutorial on Thompson SamplingCode1
The Impact of Typicality for Informative Representative Selection0
Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally0
Personalizing Gesture Recognition Using Hierarchical Bayesian Neural Networks0
Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback0
Active Sentiment Domain Adaptation0
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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