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 22312240 of 3073 papers

TitleStatusHype
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models0
Active Community Detection with Maximal Expected Model Change0
Active Continual Learning: On Balancing Knowledge Retention and Learnability0
Active covariance estimation by random sub-sampling of variables0
Active Covering0
Active Crowd Counting with Limited Supervision0
Active Curriculum Learning0
Active Data Discovery: Mining Unknown Data using Submodular Information Measures0
Active Deep Decoding of Linear Codes0
Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification0
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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