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 26412650 of 3073 papers

TitleStatusHype
AI For Fraud Awareness0
AI-Guided Defect Detection Techniques to Model Single Crystal Diamond Growth0
AI-Guided Feature Segmentation Techniques to Model Features from Single Crystal Diamond Growth0
AKF-SR: Adaptive Kalman Filtering-based Successor Representation0
A Lagrangian Duality Approach to Active Learning0
ALANNO: An Active Learning Annotation System for Mortals0
ALARM: Active LeArning of Rowhammer Mitigations0
ALdataset: a benchmark for pool-based active learning0
ALEVS: Active Learning by Statistical Leverage Sampling0
ALEX: Active Learning based Enhancement of a Model's Explainability0
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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