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 1651–1675 of 3073 papers

TitleStatusHype
Uncertainty and Traffic-Aware Active Learning for Semantic Parsing—0
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue—0
Uncertainty Aware Active Learning for Reconfiguration of Pre-trained Deep Object-Detection Networks for New Target Domains—0
Uncertainty-aware Active Learning for Optimal Bayesian Classifier—0
Uncertainty-aware Active Learning of NeRF-based Object Models for Robot Manipulators using Visual and Re-orientation Actions—0
Uncertainty Based Active Learning Strategy for Interactive Weakly Supervised Learning through Data Programming—0
Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables—0
Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning—0
Uncertainty Driven Active Learning for Image Segmentation in Underwater Inspection—0
Uncertainty-Error correlations in Evidential Deep Learning models for biomedical segmentation—0
Uncertainty Estimation for Language Reward Models—0
Uncertainty for Active Learning on Graphs—0
Uncertainty Herding: One Active Learning Method for All Label Budgets—0
Uncertainty in Natural Language Generation: From Theory to Applications—0
Uncertainty Meets Diversity: A Comprehensive Active Learning Framework for Indoor 3D Object Detection—0
Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks—0
Uncertainty quantification and exploration-exploitation trade-off in humans—0
Uncertainty Quantification in Continual Open-World Learning—0
Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles—0
Uncertainty Sentence Sampling by Virtual Adversarial Perturbation—0
Unlocking the Power of LLM Uncertainty for Active In-Context Example Selection—0
Understand customer reviews with less data and in short time: pretrained language representation and active learning—0
Understanding Approximation for Bayesian Inference in Neural Networks—0
Understanding Discourse on Work and Job-Related Well-Being in Public Social Media—0
Understanding Goal-Oriented Active Learning via Influence Functions—0
Show:102550
← PrevPage 67 of 123Next →

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