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 15711580 of 3073 papers

TitleStatusHype
Near-optimal inference in adaptive linear regression0
Near-optimality for infinite-horizon restless bandits with many arms0
Needle in a Haystack: Reducing the Costs of Annotating Rare-Class Instances in Imbalanced Datasets0
NepTrain and NepTrainKit: Automated Active Learning and Visualization Toolkit for Neuroevolution Potentials0
Neural Active Learning Beyond Bandits0
Neural Active Learning Meets the Partial Monitoring Framework0
Neural Active Learning with Performance Guarantees0
Neural Network-Based Active Learning in Multivariate Calibration0
Neural Window Decoder for SC-LDPC Codes0
NeuroADDA: Active Discriminative Domain Adaptation in Connectomic0
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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