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 1471–1480 of 3073 papers

TitleStatusHype
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation—0
Discwise Active Learning for LiDAR Semantic Segmentation—0
Discriminative Batch Mode Active Learning—0
Embodied Visual Active Learning for Semantic Segmentation—0
Empirical Evaluation of Active Learning Techniques for Neural MT—0
Empirical Evaluations of Active Learning Strategies in Legal Document Review—0
Discriminative Active Learning for Domain Adaptation—0
Empowering Language Models with Active Inquiry for Deeper Understanding—0
An active learning approach for improving the performance of equilibrium based chemical simulations—0
Discrepancy-based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images—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