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

Showing 13411350 of 3073 papers

TitleStatusHype
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification0
Nonstationary data stream classification with online active learning and siamese neural networksCode0
Robust Active Distillation0
Improved Algorithms for Neural Active LearningCode0
Detecting Minority Arguments for Mutual Understanding: A Moderation Tool for the Online Climate Change DebateCode0
Improving Generative Flow Networks with Path Regularization0
Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data0
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings0
From Weakly Supervised Learning to Active Learning0
Smart Active Sampling to enhance Quality Assurance Efficiency0
Show:102550
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified