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 1241–1250 of 3073 papers

TitleStatusHype
A Review of Machine Learning Methods Applied to Video Analysis Systems—0
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation—0
Active learning of timed automata with unobservable resets—0
A Review and A Robust Framework of Data-Efficient 3D Scene Parsing with Traditional/Learned 3D Descriptors—0
Active learning of the thermodynamics-dynamics tradeoff in protein condensates—0
Are Good Explainers Secretly Human-in-the-Loop Active Learners?—0
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—0
Active Learning of SVDD Hyperparameter Values—0
Are All Training Examples Created Equal? An Empirical Study—0
Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations—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