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 1671–1680 of 3073 papers

Title	Date	Tasks	Status	Hype
Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation	Jun 25, 2021	Active LearningUncertainty Quantification	—Unverified	0
TableSense: Spreadsheet Table Detection with Convolutional Neural Networks	Jun 25, 2021	Active LearningBoundary Detection	CodeCode Available	1
Multi-Domain Active Learning: Literature Review and Comparative Study	Jun 25, 2021	Active Learning	CodeCode Available	0
TagRuler: Interactive Tool for Span-Level Data Programming by Demonstration	Jun 24, 2021	Active LearningDocument Classification	CodeCode Available	1
Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models	Jun 22, 2021	Active LearningNumerical Integration	CodeCode Available	0
Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outliers	Jun 22, 2021	Active Learning	—Unverified	0
A Practical & Unified Notation for Information-Theoretic Quantities in ML	Jun 22, 2021	Active LearningExperimental Design	—Unverified	0
MEAL: Manifold Embedding-based Active Learning	Jun 22, 2021	Active LearningAutonomous Driving	—Unverified	0
Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection	Jun 22, 2021	Active LearningAll	—Unverified	0
Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning	Jun 22, 2021	Active Learning	CodeCode Available	1

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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