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 13511360 of 3073 papers

TitleStatusHype
Active learning machine learns to create new quantum experiments0
Active Covering0
An Analysis of Active Learning With Uniform Feature Noise0
An Analysis and Visualization Tool for Case Study Learning of Linguistic Concepts0
Active Learning in Video Tracking0
Analyzing Well-Formedness of Syllables in Japanese Sign Language0
ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning0
Active Learning For Contextual Linear Optimization: A Margin-Based Approach0
Active Learning Classification from a Signal Separation Perspective0
Analytic Mutual Information in Bayesian Neural Networks0
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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