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 2911–2920 of 3073 papers

TitleStatusHype
Active Model Aggregation via Stochastic Mirror Descent—0
Neural Network-Based Active Learning in Multivariate Calibration—0
What Properties are Desirable from an Electron Microscopy Segmentation Algorithm—0
Efficient Learning of Linear Separators under Bounded Noise—0
Joint Active Learning with Feature Selection via CUR Matrix Decomposition—0
Just Sort It! A Simple and Effective Approach to Active Preference Learning—0
Gaussian Process Models for HRTF based Sound-Source Localization and Active-Learning—0
Estimating Optimal Active Learning via Model Retraining Improvement—0
Stochastic Descent Analysis of Representation Learning Algorithms—0
Visual Causal Feature 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