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 961–970 of 3073 papers

TitleStatusHype
Benchmarking Active Learning Strategies for Materials Optimization and Discovery—0
Benchmarking Multi-Domain Active Learning on Image Classification—0
Active Learning for Event Extraction with Memory-based Loss Prediction Model—0
A Graph-Based Approach for Active Learning in Regression—0
Benchmarks and Algorithms for Offline Preference-Based Reward Learning—0
Active learning for structural reliability analysis with multiple limit state functions through variance-enhanced PC-Kriging surrogate models—0
BERT-PersNER: A New Model for Persian Named Entity Recognition—0
Best Arm Identification for Contaminated Bandits—0
Best Practices in Pool-based Active Learning for Image Classification—0
Agnostic Multi-Group Active Learning—0
Show:102550
← PrevPage 97 of 308Next →

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