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 21412150 of 3073 papers

TitleStatusHype
VOILA: An Optimised Dialogue System for Interactively Learning Visually-Grounded Word Meanings (Demonstration System)0
Warm Start Active Learning with Proxy Labels \& Selection via Semi-Supervised Fine-Tuning0
Weakly Supervised Active Learning with Cluster Annotation0
Weather and Light Level Classification for Autonomous Driving: Dataset, Baseline and Active Learning0
Weight Decay Scheduling and Knowledge Distillation for Active Learning0
Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification0
Weighted Ensembles for Active Learning with Adaptivity0
Physics-informed active learning with simultaneous weak-form latent space dynamics identification0
What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper0
What can be learned from satisfaction assessments?0
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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