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 20012010 of 3073 papers

TitleStatusHype
Wireless for Machine Learning0
A Survey of Deep Active LearningCode0
Cost-Quality Adaptive Active Learning for Chinese Clinical Named Entity Recognition0
Buy Me That Look: An Approach for Recommending Similar Fashion ProductsCode0
Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of StaneneCode1
Mask-guided sample selection for Semi-Supervised Instance Segmentation0
Deep Active Learning in Remote Sensing for data efficient Change DetectionCode1
Active learning of deep surrogates for PDEs: Application to metasurface design0
Probabilistic Deep Learning for Instance Segmentation0
What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper0
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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