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 671680 of 3073 papers

TitleStatusHype
A Bayesian Approach for Sequence Tagging with CrowdsCode0
Double Q-PID algorithm for mobile robot controlCode0
Bayesian Dark KnowledgeCode0
An Active Learning Reliability Method for Systems with Partially Defined Performance FunctionsCode0
DUAL: Diversity and Uncertainty Active Learning for Text SummarizationCode0
EdgeAL: An Edge Estimation Based Active Learning Approach for OCT SegmentationCode0
Amortized Inference for Gaussian Process Hyperparameters of Structured KernelsCode0
Active learning in annotating micro-blogs dealing with e-reputationCode0
Active Learning for Non-Parametric Regression Using Purely Random TreesCode0
Bayesian Batch Active Learning as Sparse Subset ApproximationCode0
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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