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 17011710 of 3073 papers

TitleStatusHype
Importance sampling based active learning for parametric seismic fragility curve estimation0
AdjointNet: Constraining machine learning models with physics-based codes0
Active Learning for Automated Visual Inspection of Manufactured Products0
Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification0
Data-Driven Wind Turbine Wake Modeling via Probabilistic Machine Learning0
Sample Noise Impact on Active LearningCode0
ALLWAS: Active Learning on Language models in WASserstein space0
Active Learning for Interactive Relation Extraction in a French Newspaper’s Articles0
BERT-PersNER: A New Model for Persian Named Entity Recognition0
Word Discriminations for Vocabulary Inventory PredictionCode0
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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