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 921–930 of 3073 papers

TitleStatusHype
Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural NetworksCode0
Perturbation-based Active Learning for Question Answering—0
Active Learning-Based Species Range EstimationCode0
Re-weighting Tokens: A Simple and Effective Active Learning Strategy for Named Entity Recognition—0
Incentivized Collaboration in Active Learning—0
Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation—0
Image Restoration with Point Spread Function Regularization and Active Learning—0
A Scalable Training Strategy for Blind Multi-Distribution Noise Removal—0
A Planning-and-Exploring Approach to Extreme-Mechanics Force Fields—0
Model Uncertainty based Active Learning on Tabular Data using Boosted Trees—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