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 21012110 of 3073 papers

TitleStatusHype
GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networks0
Goal-Driven Dynamics Learning via Bayesian Optimization0
Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Review0
GPIRT: A Gaussian Process Model for Item Response Theory0
Gradient Methods for Submodular Maximization0
Graph-Based Active Learning: A New Look at Expected Error Minimization0
Graph-based Active Learning for Entity Cluster Repair0
Graph-based Active Learning for Surface Water and Sediment Detection in Multispectral Images0
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks0
Class-Balanced and Reinforced Active Learning on Graphs0
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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