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 15111520 of 3073 papers

TitleStatusHype
Convergence of Uncertainty Sampling for Active Learning0
RIM: Reliable Influence-based Active Learning on GraphsCode0
Teaching an Active Learner with Contrastive Examples0
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Failure-averse Active Learning for Physics-constrained Systems0
Diversity Enhanced Active Learning with Strictly Proper Scoring RulesCode1
Active-LATHE: An Active Learning Algorithm for Boosting the Error Exponent for Learning Homogeneous Ising TreesCode0
Confidence-Aware Active Feedback for Interactive Instance SearchCode0
A Simple Baseline for Low-Budget Active LearningCode1
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity0
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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