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 13211330 of 3073 papers

TitleStatusHype
Designing and Contextualising Probes for African Languages0
Design of an Active Learning System with Human Correction for Content Analysis0
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout0
Confidence-based Active Learning Methods for Machine Translation0
ActiveLab: Active Learning with Re-Labeling by Multiple Annotators0
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study0
Cell Library Characterization for Composite Current Source Models Based on Gaussian Process Regression and Active Learning0
Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge0
Detecting Repeating Objects using Patch Correlation Analysis0
Discovering Knowledge Graph Schema from Short Natural Language Text via Dialog0
Show:102550
← PrevPage 133 of 308Next →

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