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 20412050 of 3073 papers

TitleStatusHype
Fine-Grained Product Class Recognition for Assisted Shopping0
FINETUNA: Fine-tuning Accelerated Molecular Simulations0
Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning0
Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally0
FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression0
FITAnnotator: A Flexible and Intelligent Text Annotation System0
Flattening a Hierarchical Clustering through Active Learning0
Flexible image analysis for law enforcement agencies with deep neural networks to determine: where, who and what0
Focused Active Learning for Histopathological Image Classification0
Focusing Annotation for Semantic Role Labeling0
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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