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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 961–970 of 3073 papers

TitleStatusHype
Deterministic Langevin Unconstrained Optimization with Normalizing Flows—0
Deep Active Learning with Noisy Oracle in Object Detection—0
Nonparametric active learning for cost-sensitive classification—0
Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition—0
Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression—0
Gradient and Uncertainty Enhanced Sequential Sampling for Global FitCode0
Multi-Resolution Active Learning of Fourier Neural OperatorsCode0
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling—0
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory ExplorationCode0
Frugal Satellite Image Change Detection with Deep-Net Inversion—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified