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 801–810 of 3073 papers

TitleStatusHype
A Smart System to Generate and Validate Question Answer Pairs for COVID-19 Literature—0
A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation—0
Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment—0
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization—0
Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression—0
Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort—0
Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review—0
A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction—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