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 21512160 of 3073 papers

TitleStatusHype
Deep Active Learning for Biased Datasets via Fisher Kernel Self-SupervisionCode1
Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral MeasuresCode1
Streaming Active Deep Forest for Evolving Data Stream Classification0
Stealing Black-Box Functionality Using The Deep Neural Tree ArchitectureCode0
Towards Robust and Reproducible Active Learning Using Neural NetworksCode1
Information Condensing Active LearningCode0
Adaptive Region-Based Active Learning0
Reinforced active learning for image segmentationCode1
Let Me At Least Learn What You Really Like: Dealing With Noisy Humans When Learning Preferences0
On State Variables, Bandit Problems and POMDPs0
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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