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 28612870 of 3073 papers

TitleStatusHype
Learning How to Active Learn by DreamingCode0
Learning How to Actively Learn: A Deep Imitation Learning ApproachCode0
Noisy Batch Active Learning with Deterministic AnnealingCode0
Optimal Data Selection: An Online Distributed ViewCode0
Self-supervised optimization of random material microstructures in the small-data regimeCode0
Learning Linear Dynamical Systems with Semi-Parametric Least SquaresCode0
Active DOP: A constituency treebank annotation tool with online learningCode0
Transferable Candidate Proposal with Bounded UncertaintyCode0
Explainable Active Learning for Preference ElicitationCode0
Online Adaptive Asymmetric Active Learning with Limited BudgetsCode0
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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