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 27012710 of 3073 papers

TitleStatusHype
An Approach to Reducing Annotation Costs for BioNLP0
An Artificial Intelligence (AI) workflow for catalyst design and optimization0
Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization0
A Nearly Instance Optimal Algorithm for Top-k Ranking under the Multinomial Logit Model0
An Efficient Active Learning Framework for New Relation Types0
An Efficient Active Learning Pipeline for Legal Text Classification0
An Empirical Study on the Efficacy of Deep Active Learning for Image Classification0
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets0
A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions0
A New Perspective on Pool-Based Active Classification and False-Discovery Control0
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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