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 15611570 of 3073 papers

TitleStatusHype
Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning0
Multi-View Active Learning in the Non-Realizable Case0
MuRAL: Multi-Scale Region-based Active Learning for Object Detection0
Narrowing the Loop: Integration of Resources and Linguistic Dataset Development with Interactive Machine Learning0
Navigating the Maize: Cyclic and conditional computational graphs for molecular simulation0
Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles0
Near-Optimal Active Learning of Halfspaces via Query Synthesis in the Noisy Setting0
Near Optimal Bayesian Active Learning for Decision Making0
Near-optimal Bayesian Active Learning with Correlated and Noisy Tests0
Near-Optimal Bayesian Active Learning with Noisy Observations0
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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