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 26012610 of 3073 papers

TitleStatusHype
Active Generation for Image ClassificationCode0
A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about FluidsCode0
Active Fuzzing for Testing and Securing Cyber-Physical SystemsCode0
Deep Active Learning: Unified and Principled Method for Query and TrainingCode0
Adapting Coreference Resolution Models through Active LearningCode0
Deep Active Learning via Open Set RecognitionCode0
Deep Active Learning with Adaptive AcquisitionCode0
Deep Active Learning with a Neural Architecture SearchCode0
Deep Active Learning with Augmentation-based Consistency EstimationCode0
Stealing Black-Box Functionality Using The Deep Neural Tree ArchitectureCode0
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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