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

Showing 231240 of 3073 papers

TitleStatusHype
Label-Efficient Learning in Agriculture: A Comprehensive ReviewCode1
LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active LearningCode1
Learning Loss for Active LearningCode1
Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image SegmentationCode1
LLMaAA: Making Large Language Models as Active AnnotatorsCode1
LTP: A New Active Learning Strategy for CRF-Based Named Entity RecognitionCode1
Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcityCode1
Making Better Use of Unlabelled Data in Bayesian Active LearningCode1
A Benchmark on Uncertainty Quantification for Deep Learning PrognosticsCode1
ActiveGLAE: A Benchmark for Deep Active Learning with TransformersCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified