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 1971–1980 of 3073 papers

TitleStatusHype
Active Output Selection Strategies for Multiple Learning Regression Models—0
A smartphone based multi input workflow for non-invasive estimation of haemoglobin levels using machine learning techniques—0
Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient—0
Active Learning in CNNs via Expected Improvement MaximizationCode0
Low-Resolution Face Recognition In Resource-Constrained Environments—0
Cost-effective Variational Active Entity Resolution—0
Finding the Homology of Decision Boundaries with Active LearningCode0
SAFARI: Safe and Active Robot Imitation Learning with Imagination—0
Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness—0
A Transfer Learning Based Active Learning Framework for Brain Tumor Classification—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