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 21–30 of 3073 papers

TitleStatusHype
Active Learning via Regression Beyond Realizability—0
Extending AALpy with Passive Learning: A Generalized State-Merging Approach—0
Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted NetworksCode0
Aurora: Are Android Malware Classifiers Reliable and Stable under Distribution Shift?—0
Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event DetectionCode0
Active Learning-Enhanced Dual Control for Angle-Only Initial Relative Orbit Determination—0
MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate ModelsCode0
Language Model-Enhanced Message Passing for Heterophilic Graph Learning—0
Exploring the Possibility of TypiClust for Low-Budget Federated Active Learning—0
Monocle: Hybrid Local-Global In-Context Evaluation for Long-Text Generation with Uncertainty-Based Active Learning—0
Show:102550
← PrevPage 3 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