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 2071–2080 of 3073 papers

TitleStatusHype
Functional MRI applications for psychiatric disease subtyping: a review—0
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles—0
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes—0
GALOT: Generative Active Learning via Optimizable Zero-shot Text-to-image Generation—0
Gamifying optimization: a Wasserstein distance-based analysis of human search—0
Gaussian-Process-based Adaptive Tracking Control with Dynamic Active Learning for Autonomous Ground Vehicles—0
Gaussian Process Classification Bandits—0
Gaussian Process Meta-Representations For Hierarchical Neural Network Weight Priors—0
Gaussian Process Meta-Representations Of Neural Networks—0
Gaussian Process Models for HRTF based Sound-Source Localization and Active-Learning—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