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 22612270 of 3073 papers

TitleStatusHype
K-nn active learning under local smoothness condition0
K-NN active learning under local smoothness assumption0
Knowledge Completion for Generics using Guided Tensor Factorization0
Autonomous Wireless Systems with Artificial Intelligence0
Knowledge Modelling and Active Learning in Manufacturing0
Label Distribution Learning using the Squared Neural Family on the Probability Simplex0
Label-Efficient Interactive Time-Series Anomaly Detection0
Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach0
Label-efficient Single Photon Images Classification via Active Learning0
LADA: Look-Ahead Data Acquisition via Augmentation for Active 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