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 2176–2200 of 3073 papers

TitleStatusHype
Improve Cost Efficiency of Active Learning over Noisy Dataset—0
Improved Active Learning via Dependent Leverage Score Sampling—0
Improved active output selection strategy for noisy environments—0
Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler—0
Improved Algorithms for Agnostic Pool-based Active Classification—0
Improved Algorithms for Efficient Active Learning Halfspaces with Massart and Tsybakov noise—0
Improving Active Learning in Systematic Reviews—0
Improving Active Learning with a Bayesian Representation of Epistemic Uncertainty—0
Improving Classification-Based Natural Language Understanding with Non-Expert Annotation—0
Improving Classification Performance With Human Feedback: Label a few, we label the rest—0
Improving Classification through Weak Supervision in Context-specific Conversational Agent Development for Teacher Education—0
Improving Data Augmentation in Low-resource Question Answering with Active Learning in Multiple Stages—0
Improving decision-making via risk-based active learning: Probabilistic discriminative classifiers—0
Improving Differentially Private Models with Active Learning—0
Improving Event Detection with Active Learning—0
Improving Generative Flow Networks with Path Regularization—0
Improving greedy core-set configurations for active learning with uncertainty-scaled distances—0
Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagery—0
Improving Model-Based Control and Active Exploration with Reconstruction Uncertainty Optimization—0
Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries—0
Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop—0
Improving performance of aircraft detection in satellite imagery while limiting the labelling effort: Hybrid active learning—0
Improving Probabilistic Models in Text Classification via Active Learning—0
Robust Contrastive Active Learning with Feature-guided Query Strategies—0
Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining—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