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 531–540 of 3073 papers

TitleStatusHype
Active Deep Kernel Learning of Molecular Functionalities: Realizing Dynamic Structural Embeddings—0
Active Learning of Quantum System Hamiltonians yields Query Advantage—0
Active Learning of Piecewise Gaussian Process Surrogates—0
Active Learning for Automated Visual Inspection of Manufactured Products—0
A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching—0
Bayesian Active Learning for Semantic Segmentation—0
A Deep Learning Driven Active Framework for Segmentation of Large 3D Shape Collections—0
ADROIT: A Self-Supervised Framework for Learning Robust Representations for Active Learning—0
Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression—0
AI-Guided Feature Segmentation Techniques to Model Features from Single Crystal Diamond Growth—0
Show:102550
← PrevPage 54 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