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 291300 of 3073 papers

TitleStatusHype
A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning0
Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study0
Distribution Discrepancy and Feature Heterogeneity for Active 3D Object DetectionCode0
Interactive Machine Teaching by Labeling Rules and Instances0
Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity0
Active learning for regression in engineering populations: A risk-informed approach0
MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search0
An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting0
Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait SketchingCode0
Adaptive Open-Set Active Learning with Distance-Based Out-of-Distribution Detection for Robust Task-Oriented Dialog SystemCode0
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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