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 2376–2400 of 3073 papers

TitleStatusHype
Active Learning for Nonlinear System Identification with Guarantees—0
Active Learning for Non-Parametric Choice Models—0
Active learning for object detection in high-resolution satellite images—0
Active Learning for Object Detection with Non-Redundant Informative Sampling—0
Active Learning for One-Class Classification Using Two One-Class Classifiers—0
Active Learning for Online Recognition of Human Activities from Streaming Videos—0
Active learning for imbalanced data under cold start—0
Active Learning for Phenotyping Tasks—0
Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning—0
Active Learning for Post-Editing Based Incrementally Retrained MT—0
Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion—0
Active Learning for Product Type Ontology Enhancement in E-commerce—0
Active Learning for Regression by Inverse Distance Weighting—0
Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks—0
Active learning for regression in engineering populations: A risk-informed approach—0
Active Learning for Regression with Aggregated Outputs—0
Active Learning For Repairable Hardware Systems With Partial Coverage—0
Active Learning for Risk-Sensitive Inverse Reinforcement Learning—0
Active Learning for Robust and Representative LLM Generation in Safety-Critical Scenarios—0
Active Learning for Rumor Identification on Social Media—0
Active Learning for Segmentation Based on Bayesian Sample Queries—0
Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy—0
Active learning for sense annotation—0
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates—0
Active Learning for Single Neuron Models with Lipschitz Non-Linearities—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