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 23912400 of 3073 papers

TitleStatusHype
Mean-Variance Analysis in Bayesian Optimization under Uncertainty0
MEAOD: Model Extraction Attack against Object Detectors0
Measuring Mother-Infant Emotions By Audio Sensing0
Mechanics and Design of Metastructured Auxetic Patches with Bio-inspired Materials0
MedAL: Deep Active Learning Sampling Method for Medical Image Analysis0
MedCAT -- Medical Concept Annotation Tool0
MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation0
Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions0
MelissaDL x Breed: Towards Data-Efficient On-line Supervised Training of Multi-parametric Surrogates with Active Learning0
Message Passing Adaptive Resonance Theory for Online Active Semi-supervised 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