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 29913000 of 3073 papers

TitleStatusHype
Frugal Algorithm SelectionCode0
Active Learning for Entity Filtering in Microblog StreamsCode0
Active Learning for Entity AlignmentCode0
LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model TrainingCode0
Towards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian Optimization: Current challenges and future opportunitiesCode0
LRTD: Long-Range Temporal Dependency based Active Learning for Surgical Workflow RecognitionCode0
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity RecognizersCode0
LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus ImagesCode0
Fuzziness-based Spatial-Spectral Class Discriminant Information Preserving Active Learning for Hyperspectral Image ClassificationCode0
GALAXY: Graph-based Active Learning at the ExtremeCode0
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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