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 201–225 of 3073 papers

TitleStatusHype
Active Imitation Learning with Noisy GuidanceCode1
Active Learning for Computationally Efficient Distribution of Binary Evolution SimulationsCode1
A comprehensive survey on deep active learning in medical image analysisCode1
Active Learning Through a Covering LensCode1
Enhanced Multi-Object Tracking Using Pose-based Virtual Markers in 3x3 BasketballCode1
Entropic Open-set Active LearningCode1
Active Learning for Convolutional Neural Networks: A Core-Set ApproachCode1
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive ProcessesCode1
Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of StaneneCode1
Active Learning for Coreference Resolution using Discrete AnnotationCode1
Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral MeasuresCode1
Finding active galactic nuclei through FinkCode1
Active Domain Adaptation via Clustering Uncertainty-weighted EmbeddingsCode1
Fink: early supernovae Ia classification using active learningCode1
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)Code1
Active Learning Meets Optimized Item SelectionCode1
Generating π-Functional Molecules Using STGG+ with Active LearningCode1
Active Learning on a Budget: Opposite Strategies Suit High and Low BudgetsCode1
Active Learning for Deep Object Detection via Probabilistic ModelingCode1
Gone Fishing: Neural Active Learning with Fisher EmbeddingsCode1
Graph Policy Network for Transferable Active Learning on GraphsCode1
Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic SegmentationCode1
Active Learning for Optimal Intervention Design in Causal ModelsCode1
Active Learning for Open-set AnnotationCode1
Active Learning from the WebCode1
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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