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 1921–1930 of 3073 papers

TitleStatusHype
Machine Learning Algorithms for Data Labeling: An Empirical Evaluation—0
Active Learning for Lane Detection: A Knowledge Distillation Approach—0
Efficiently labelling sequences using semi-supervised active learning—0
Learning Rare Category Classifiers on a Tight Labeling Budget—0
Least Probable Disagreement Region for Active Learning—0
Uncertainty-aware Active Learning for Optimal Bayesian Classifier—0
Active Universal Domain Adaptation—0
SoCal: Selective Oracle Questioning for Consistency-based Active Learning of Physiological Signals—0
Better Optimization can Reduce Sample Complexity: Active Semi-Supervised Learning via Convergence Rate Control—0
Crowd Counting With Partial Annotations in an ImageCode0
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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