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 17211730 of 3073 papers

TitleStatusHype
What Makes Good Few-shot Examples for Vision-Language Models?0
What Properties are Desirable from an Electron Microscopy Segmentation Algorithm0
WHAT TO DO IF SPARSE REPRESENTATION LEARNING FAILS UNEXPECTEDLY?0
What to Prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development0
Black-box Generalization of Machine Teaching0
When Deep Learners Change Their Mind: Learning Dynamics for Active Learning0
When does Active Learning Work?0
When Your Robot Breaks: Active Learning During Plant Failure0
Whom to Test? Active Sampling Strategies for Managing COVID-190
Wireless for Machine 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