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 91–100 of 3073 papers

TitleStatusHype
Active Learning For Repairable Hardware Systems With Partial Coverage—0
Uncertainty Meets Diversity: A Comprehensive Active Learning Framework for Indoor 3D Object Detection—0
Efficient Data Selection for Training Genomic Perturbation Models—0
Active Learning from Scene Embeddings for End-to-End Autonomous Driving—0
Preference Elicitation for Multi-objective Combinatorial Optimization with Active Learning and Maximum Likelihood Estimation—0
Have LLMs Made Active Learning Obsolete? Surveying the NLP Community—0
Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets—0
Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and SolutionsCode0
QuickDraw: Fast Visualization, Analysis and Active Learning for Medical Image SegmentationCode0
Generative method for aerodynamic optimization based on classifier-free guided denoising diffusion probabilistic model—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