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 2471–2480 of 3073 papers

TitleStatusHype
Active Learning on Medical Image—0
Active Learning on Synthons for Molecular Design—0
Active Learning On Weighted Graphs Using Adaptive And Non-adaptive Approaches—0
Active Learning over DNN: Automated Engineering Design Optimization for Fluid Dynamics Based on Self-Simulated Dataset—0
Active Learning Over Multiple Domains in Natural Language Tasks—0
Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment—0
Active Learning Polynomial Threshold Functions—0
Active Learning Principles for In-Context Learning with Large Language Models—0
Active Learning: Problem Settings and Recent Developments—0
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity—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