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 226–250 of 3073 papers

TitleStatusHype
infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-informationCode1
Is segmentation uncertainty useful?Code1
Iterative Loop Method Combining Active and Semi-Supervised Learning for Domain Adaptive Semantic SegmentationCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
Knowledge-Aware Federated Active Learning with Non-IID DataCode1
LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active LearningCode1
Learning compositional models of robot skills for task and motion planningCode1
Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image SegmentationCode1
Active Learning Helps Pretrained Models Learn the Intended TaskCode1
LTP: A New Active Learning Strategy for CRF-Based Named Entity RecognitionCode1
Machine-learning-accelerated simulations to enable automatic surface reconstructionCode1
Making Better Use of Unlabelled Data in Bayesian Active LearningCode1
Making Your First Choice: To Address Cold Start Problem in Vision Active LearningCode1
A Benchmark on Uncertainty Quantification for Deep Learning PrognosticsCode1
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question AnsweringCode1
MoBYv2AL: Self-supervised Active Learning for Image ClassificationCode1
Model Assertions for Monitoring and Improving ML ModelsCode1
Active Learning from the WebCode1
Active Learning Meets Optimized Item SelectionCode1
Multi-Objective GFlowNetsCode1
Multiple instance active learning for object detectionCode1
Learning Loss for Active LearningCode1
Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance AssessmentCode1
ActiveGLAE: A Benchmark for Deep Active Learning with TransformersCode1
Active learning for medical image segmentation with stochastic batchesCode1
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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