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 13911400 of 3073 papers

TitleStatusHype
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte CarloCode1
Improving greedy core-set configurations for active learning with uncertainty-scaled distances0
A Lagrangian Duality Approach to Active Learning0
Active Learning on a Budget: Opposite Strategies Suit High and Low BudgetsCode1
LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentation0
Improving Probabilistic Models in Text Classification via Active Learning0
Active metric learning and classification using similarity queries0
Ranking with Confidence for Large Scale Comparison DataCode0
GALAXY: Graph-based Active Learning at the ExtremeCode0
Active Multi-Task Representation Learning0
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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