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 26412650 of 3073 papers

TitleStatusHype
ACTIVETHIEF: Model Extraction Using Active Learning and Unannotated Public DataCode0
Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active LearningCode0
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-LearningCode0
Active Learning via Membership Query Synthesis for Semi-Supervised Sentence ClassificationCode0
Improving Question Answering Performance Using Knowledge Distillation and Active LearningCode0
Active learning via informed search in movement parameter space for efficient robot task learning and transferCode0
Multi-Label Bayesian Active Learning with Inter-Label RelationshipsCode0
Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processingCode0
Improving traffic sign recognition by active searchCode0
Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender SystemsCode0
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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