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 11511160 of 3073 papers

TitleStatusHype
An Active Learning Reliability Method for Systems with Partially Defined Performance FunctionsCode0
Making Your First Choice: To Address Cold Start Problem in Vision Active LearningCode1
DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures0
Active Learning for Regression with Aggregated Outputs0
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification0
Robust Active Distillation0
Nonstationary data stream classification with online active learning and siamese neural networksCode0
Improved Algorithms for Neural Active LearningCode0
Detecting Minority Arguments for Mutual Understanding: A Moderation Tool for the Online Climate Change DebateCode0
Improving Generative Flow Networks with Path Regularization0
Show:102550
← PrevPage 116 of 308Next →

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