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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1641–1650 of 3073 papers

TitleStatusHype
On the Pros and Cons of Active Learning for Moral Preference Elicitation—0
On the Query Strategies for Efficient Online Active Distillation—0
On the Robustness of Active Learning—0
On the Topology Awareness and Generalization Performance of Graph Neural Networks—0
On the use of uncertainty in classifying Aedes Albopictus mosquitoes—0
On the Utility of Active Instance Selection for Few-Shot Learning—0
On Using Active Learning and Self-Training when Mining Performance Discussions on Stack Overflow—0
On weighted uncertainty sampling in active learning—0
OPAD: An Optimized Policy-based Active Learning Framework for Document Content Analysis—0
Offline Preference-Based Apprenticeship Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified