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 28412850 of 3073 papers

TitleStatusHype
Submodular Learning and Covering with Response-Dependent Costs0
Online Active Linear Regression via Thresholding0
Interactive algorithms: from pool to stream0
Active Learning Algorithms for Graphical Model Selection0
Font Identification in Historical Documents Using Active Learning0
A Robust UCB Scheme for Active Learning in Regression from Strategic Crowds0
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models0
Self-Excitation: An Enabler for Online Thermal Estimation and Model Predictive Control of Buildings0
The Utility of Abstaining in Binary Classification0
Refined Error Bounds for Several Learning Algorithms0
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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