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 27712780 of 3073 papers

TitleStatusHype
Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression0
Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort0
Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review0
Assorted, Archetypal and Annotated Two Million (3A2M) Cooking Recipes Dataset based on Active Learning0
A strong converse bound for multiple hypothesis testing, with applications to high-dimensional estimation0
A Structured Perspective of Volumes on Active Learning0
A supervised active learning method for identifying critical nodes in Wireless Sensor Network0
A survey of active learning algorithms for supervised remote sensing image classification0
A Survey of Active Learning for Natural Language Processing0
A Survey of Active Learning for Text Classification using Deep Neural Networks0
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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