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 18111820 of 3073 papers

TitleStatusHype
A Unified Batch Selection Policy for Active Metric Learning0
Improved Algorithms for Efficient Active Learning Halfspaces with Massart and Tsybakov noise0
Bounded Memory Active Learning through Enriched Queries0
Counterfactual Contextual Multi-Armed Bandit: a Real-World Application to Diagnose Apple Diseases0
Model Rectification via Unknown Unknowns Extraction from Deployment Samples0
Active learning for distributionally robust level-set estimation0
Uncertainty quantification and exploration-exploitation trade-off in humans0
HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition0
FOIT: Fast Online Instance Transfer for Improved EEG Emotion RecognitionCode0
Teaching Digital Signal Processing by Partial Flipping, Active Learning and Visualization0
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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