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 1641–1650 of 3073 papers

TitleStatusHype
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers—0
A-Optimal Active LearningCode0
Active Learning for Deep Visual Tracking—0
On the Importance of Effectively Adapting Pretrained Language Models for Active Learning—0
Nuances in Margin Conditions Determine Gains in Active Learning—0
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue—0
Deep Active Learning by Leveraging Training Dynamics—0
Knowledge-driven Active LearningCode0
An active learning approach for improving the performance of equilibrium based chemical simulations—0
Streaming Machine Learning and Online Active Learning for Automated Visual Inspection—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