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 576–600 of 3073 papers

TitleStatusHype
Reactive Multi-Robot Navigation in Outdoor Environments Through Uncertainty-Aware Active Learning of Human Preference Landscape—0
Open-/Closed-loop Active Learning for Data-driven Predictive Control—0
From Passive Watching to Active Learning: Empowering Proactive Participation in Digital Classrooms with AI Video Assistant—0
CAMAL: Optimizing LSM-trees via Active LearningCode0
Critic Loss for Image Classification—0
The trade-off between data minimization and fairness in collaborative filtering—0
Enhancing Semi-Supervised Learning via Representative and Diverse Sample SelectionCode0
SANE: Strategic Autonomous Non-Smooth Exploration for Multiple Optima Discovery in Multi-modal and Non-differentiable Black-box Functions—0
Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning—0
Active learning for energy-based antibody optimization and enhanced screening—0
Active Learning to Guide Labeling Efforts for Question Difficulty EstimationCode0
MALADY: Multiclass Active Learning with Auction Dynamics on Graphs—0
DEMAU: Decompose, Explore, Model and Analyse Uncertainties—0
Crown-Like Structures in Breast Adipose Tissue: Finding a 'Needle-in-a-Haystack' using Artificial Intelligence and Collaborative Active Learning on the Web—0
FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression—0
Automated Discovery of Pairwise Interactions from Unstructured Data—0
STAND: Data-Efficient and Self-Aware Precondition Induction for Interactive Task Learning—0
A Scalable Algorithm for Active Learning—0
A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning—0
Bounds on the Generalization Error in Active Learning—0
Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study—0
Distribution Discrepancy and Feature Heterogeneity for Active 3D Object DetectionCode0
Interactive Machine Teaching by Labeling Rules and Instances—0
Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity—0
Active learning for regression in engineering populations: A risk-informed approach—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