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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 1161–1170 of 3073 papers

Title	Date	Tasks	Status
Active Learning using Deep Bayesian Networks for Surgical Workflow Analysis	Nov 8, 2018	Active LearningBIG-bench Machine Learning	—Unverified
Asymptotic Analysis of Objectives based on Fisher Information in Active Learning	May 27, 2016	Active Learning	—Unverified
Asymptotic Accuracy of Distribution-Based Estimation for Latent Variables	Apr 10, 2012	Active LearningModel Selection	—Unverified
Active learning using adaptable task-based prioritisation	Dec 3, 2022	Active LearningMeta Reinforcement Learning	—Unverified
A Survey on Uncertainty Quantification Methods for Deep Learning	Feb 26, 2023	Active LearningAutonomous Driving	—Unverified
A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams	Jun 16, 2021	Active LearningBenchmarking	—Unverified
Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outliers	Jun 22, 2021	Active Learning	—Unverified
Active Learning for Coreference Resolution	Jun 1, 2012	Active Learningcoreference-resolution	—Unverified
A Compression Technique for Analyzing Disagreement-Based Active Learning	Apr 5, 2014	Active Learning	—Unverified
A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning	Sep 10, 2024	Active LearningObject	—Unverified

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Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	TypiClust	Accuracy	93.2	—	Unverified
2	PT4AL	Accuracy	93.1	—	Unverified
3	Learning loss	Accuracy	91.01	—	Unverified
4	CoreGCN	Accuracy	90.7	—	Unverified
5	Core-set	Accuracy	89.92	—	Unverified
6	Random Baseline (Resnet18)	Accuracy	88.45	—	Unverified
7	Random Baseline (VGG16)	Accuracy	85.09	—	Unverified