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 831–840 of 3073 papers

TitleStatusHype
Automatic Segmentation of the Spinal Cord Nerve RootletsCode0
ActDroid: An active learning framework for Android malware detection—0
The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration—0
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
Multitask Active Learning for Graph Anomaly DetectionCode0
Falcon: Fair Active Learning using Multi-armed BanditsCode0
Learning from the Best: Active Learning for Wireless Communications—0
Show:102550
← PrevPage 84 of 308Next →

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