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 29012910 of 3073 papers

TitleStatusHype
Social Media Predictive Analytics0
Active Learning with Rationales for Text Classification0
Removing the Training Wheels: A Coreference Dataset that Entertains Humans and Challenges Computers0
Hierarchical Subquery Evaluation for Active Learning on a Graph0
An Active Learning Based Approach For Effective Video Annotation And Retrieval0
Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Review0
Analysis of Stopping Active Learning based on Stabilizing Predictions0
Towards a relation extraction framework for cyber-security conceptsCode0
Deciding when to stop: Efficient stopping of active learning guided drug-target prediction0
A New Vision of Collaborative Active Learning0
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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