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 18011810 of 3073 papers

TitleStatusHype
Active learning based generative design for the discovery of wide bandgap materialsCode1
Active Selection of Classification FeaturesCode0
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification0
Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography0
SISE-PC: Semi-supervised Image Subsampling for Explainable PathologyCode1
Deep Deterministic Uncertainty: A Simple BaselineCode1
Nonparametric adaptive active learning under local smoothness condition0
Interpret-able feedback for AutoML systems0
Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task HierarchyCode0
DEUP: Direct Epistemic Uncertainty PredictionCode1
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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