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 1771–1780 of 3073 papers

TitleStatusHype
Multi-Domain Active Learning: Literature Review and Comparative StudyCode0
Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation—0
Distributional Gradient Matching for Learning Uncertain Neural Dynamics ModelsCode0
Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outliers—0
MEAL: Manifold Embedding-based Active Learning—0
A Practical & Unified Notation for Information-Theoretic Quantities in ML—0
Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection—0
ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument AggregationCode0
Phrase-level Active Learning for Neural Machine Translation—0
Corruption Robust Active Learning—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