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 23512360 of 3073 papers

TitleStatusHype
Discriminative Active LearningCode0
Self-Regulated Interactive Sequence-to-Sequence LearningCode0
The Power of Comparisons for Actively Learning Linear Classifiers0
A Semi-Supervised Framework for Automatic Pixel-Wise Breast Cancer Grading of Histological Images0
AlpacaTag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging0
Learning How to Active Learn by DreamingCode0
Active Learning within Constrained Environments through Imitation of an Expert Questioner0
L*-Based Learning of Markov Decision Processes (Extended Version)0
The Practical Challenges of Active Learning: Lessons Learned from Live Experimentation0
Deep Active Learning with Adaptive AcquisitionCode0
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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