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 2221–2230 of 3073 papers

TitleStatusHype
Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments—0
Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning—0
Integrating Crowdsourcing and Active Learning for Classification of Work-Life Events from Tweets—0
Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression—0
Integrating Semi-Supervised and Active Learning for Semantic Segmentation—0
Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties—0
Interactive algorithms: from pool to stream—0
Interactive Event Sifting using Bayesian Graph Neural Networks—0
Interactive Machine Teaching by Labeling Rules and Instances—0
Interactive Ontology Matching with Cost-Efficient 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