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 14111420 of 3073 papers

TitleStatusHype
Dual Adversarial Network for Deep Active Learning0
Dual Control of Exploration and Exploitation for Auto-Optimisation Control with Active Learning0
Analytic Mutual Information in Bayesian Neural Networks0
DutchSemCor: Targeting the ideal sense-tagged corpus0
Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice0
Dynamic Exploration-Exploitation Trade-Off in Active Learning Regression with Bayesian Hierarchical Modeling0
Early Forecasting of Text Classification Accuracy and F-Measure with Active Learning0
EASE: An Easily-Customized Annotation System Powered by Efficiency Enhancement Mechanisms0
Easy Questions First? A Case Study on Curriculum Learning for Question Answering0
Active and passive learning of linear separators under log-concave distributions0
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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