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 11511160 of 3073 papers

TitleStatusHype
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget0
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation0
Coresets for Classification -- Simplified and Strengthened0
Coresets for Classification – Simplified and Strengthened0
Accelerating engineering design by automatic selection of simulation cases through Pool-Based Active Learning0
DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool0
Corruption Robust Active Learning0
Adding more data does not always help: A study in medical conversation summarization with PEGASUS0
Cost-Aware Query Policies in Active Learning for Efficient Autonomous Robotic Exploration0
Data efficient deep learning for medical image analysis: A survey0
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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