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 561–570 of 3073 papers

TitleStatusHype
Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy—0
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image SegmentationCode0
A Study of Acquisition Functions for Medical Imaging Deep Active LearningCode0
Graph-based Active Learning for Entity Cluster Repair—0
Revisiting Active Learning in the Era of Vision Foundation ModelsCode1
Multitask Active Learning for Graph Anomaly DetectionCode0
Learning from the Best: Active Learning for Wireless Communications—0
Falcon: Fair Active Learning using Multi-armed BanditsCode0
MORPH: Towards Automated Concept Drift Adaptation for Malware Detection—0
Navigating the Maize: Cyclic and conditional computational graphs for molecular simulation—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