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 276–300 of 3073 papers

TitleStatusHype
The trade-off between data minimization and fairness in collaborative filtering—0
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
Enhancing Semi-Supervised Learning via Representative and Diverse Sample SelectionCode0
Active learning for energy-based antibody optimization and enhanced screening—0
Diversify and Conquer: Diversity-Centric Data Selection with Iterative RefinementCode1
Active Learning to Guide Labeling Efforts for Question Difficulty EstimationCode0
MALADY: Multiclass Active Learning with Auction Dynamics on Graphs—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
DEMAU: Decompose, Explore, Model and Analyse Uncertainties—0
A Scalable Algorithm for Active Learning—0
STAND: Data-Efficient and Self-Aware Precondition Induction for Interactive Task Learning—0
Automated Discovery of Pairwise Interactions from Unstructured Data—0
FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression—0
Bounds on the Generalization Error in Active Learning—0
A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer 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
MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search—0
An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting—0
Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait SketchingCode0
Adaptive Open-Set Active Learning with Distance-Based Out-of-Distribution Detection for Robust Task-Oriented Dialog SystemCode0
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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