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 27212730 of 3073 papers

TitleStatusHype
Active Learning for Structured Prediction from Partially Labeled Data0
Active learning machine learns to create new quantum experiments0
Non-Uniform Subset Selection for Active Learning in Structured DataCode0
Bayesian Pool-based Active Learning With Abstention Feedbacks0
Unfolding Hidden Barriers by Active Enhanced Sampling0
Active Learning for Graph EmbeddingCode0
Comments on the proof of adaptive submodular function minimization0
PANFIS++: A Generalized Approach to Evolving Learning0
Model Transfer for Tagging Low-resource Languages using a Bilingual DictionaryCode0
On Using Active Learning and Self-Training when Mining Performance Discussions on Stack Overflow0
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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