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 16511660 of 3073 papers

TitleStatusHype
ActiveEA: Active Learning for Neural Entity AlignmentCode0
Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning0
AutoNLU: Detecting, root-causing, and fixing NLU model errors0
Real-Time Learning from An Expert in Deep Recommendation Systems with Marginal Distance Probability Distribution0
Active Altruism Learning and Information Sufficiency for Autonomous Driving0
Bayesian Active Summarization0
Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up0
Active learning for interactive satellite image change detection0
Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations0
Synthesizing Video Trajectory Queries0
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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