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 30213030 of 3073 papers

TitleStatusHype
Collaborative Gaussian Processes for Preference Learning0
Active Learning of Model Evidence Using Bayesian Quadrature0
Hierarchical Optimistic Region Selection driven by Curiosity0
Active Learning of Multi-Index Function Models0
Active Learning for Chinese Word Segmentation0
Bayesian active learning with localized priors for fast receptive field characterization0
CRAB Reader: A Tool for Analysis and Visualization of Argumentative Zones in Scientific Literature0
Active Learning and the Irish Treebank0
Online allocation and homogeneous partitioning for piecewise constant mean-approximation0
Multilabel Classification using Bayesian Compressed Sensing0
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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