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 26612670 of 3073 papers

TitleStatusHype
Variational Adaptive-Newton Method for Explorative Learning0
Few-Shot Learning with Graph Neural NetworksCode0
Online Tool Condition Monitoring Based on Parsimonious Ensemble+0
Active Learning for Visual Question Answering: An Empirical StudyCode0
Deep Active Learning over the Long Tail0
A Novel Ensemble Learning Approach to Unsupervised Record Linkage0
Dependency Parsing with Partial Annotations: An Empirical Comparison0
Analyzing Well-Formedness of Syllables in Japanese Sign Language0
CADET: Computer Assisted Discovery Extraction and Translation0
Auto-Differentiating Linear Algebra0
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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