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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 421–430 of 3073 papers

TitleStatusHype
Active Learning Principles for In-Context Learning with Large Language Models—0
A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning—0
Active Learning for Cost-Sensitive Classification—0
Active Learning via Regression Beyond Realizability—0
Active Learning with a Drifting Distribution—0
Active learning with biased non-response to label requests—0
Active Learning for Community Detection in Stochastic Block Models—0
Active Learning with Constrained Topic Model—0
Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation—0
Active Deep Learning on Entity Resolution by Risk Sampling—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified