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 876–900 of 3073 papers

TitleStatusHype
Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation—0
Active Learning within Constrained Environments through Imitation of an Expert Questioner—0
AutoNLU: Detecting, root-causing, and fixing NLU model errors—0
Active Learning for Saddle Point Calculation—0
AI-Guided Feature Segmentation Techniques to Model Features from Single Crystal Diamond Growth—0
Autonomous Emergency Braking With Driver-In-The-Loop: Torque Vectoring for Active Learning—0
Autonomous synthesis of metastable materials—0
AI-Guided Defect Detection Techniques to Model Single Crystal Diamond Growth—0
AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery—0
AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems—0
AutoWS: Automated Weak Supervision Framework for Text Classification—0
Bucketized Active Sampling for Learning ACOPF—0
Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model—0
Active Learning with Neural Networks: Insights from Nonparametric Statistics—0
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning—0
Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning—0
Active Learning with Oracle Epiphany—0
Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding—0
Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems—0
AI For Fraud Awareness—0
AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor Shower Mapping—0
Active Learning for Structured Probabilistic Models With Histogram Approximation—0
AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy—0
BAOD: Budget-Aware Object Detection—0
Active Learning for Structured Prediction from Partially Labeled Data—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