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 26512660 of 3073 papers

TitleStatusHype
DeMuX: Data-efficient Multilingual LearningCode0
DenseReviewer: A Screening Prioritisation Tool for Systematic Review based on Dense RetrievalCode0
The Future of Data Science EducationCode0
Multilingual Detection of Personal Employment Status on TwitterCode0
Practical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital HumanitiesCode0
Sample Efficient Learning of Predictors that Complement HumansCode0
Derivative free optimization via repeated classificationCode0
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement PrioritizationCode0
A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TARCode0
Detecting Anatomical and Functional Connectivity Relations in Biomedical Literature via Language Representation ModelsCode0
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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