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 631–640 of 3073 papers

Title	Date	Tasks	Status	Score
Active Learning amidst Logical Knowledge	Sep 26, 2017	Active LearningBIG-bench Machine Learning	CodeCode Available	5
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture	May 22, 2023	Active LearningDecision Making	CodeCode Available	5
Black-Box Batch Active Learning for Regression	Feb 17, 2023	Active Learningregression	CodeCode Available	5
Active Learning for Top-K Rank Aggregation from Noisy Comparisons	Aug 1, 2017	Active Learning	CodeCode Available	5
A Dataset for Deep Learning-based Bone Structure Analyses in Total Hip Arthroplasty	Jun 7, 2023	Active LearningAnatomy	CodeCode Available	5
Benchmarking of Query Strategies: Towards Future Deep Active Learning	Dec 10, 2023	Active LearningBenchmarking	CodeCode Available	5
An Active Learning Reliability Method for Systems with Partially Defined Performance Functions	Oct 5, 2022	Active LearningAutonomous Driving	CodeCode Available	5
Bayesian Semi-supervised Learning with Graph Gaussian Processes	Sep 12, 2018	Active LearningGaussian Processes	CodeCode Available	5
Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models	May 10, 2024	Active LearningExperimental Design	CodeCode Available	5
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations	Feb 2, 2024	Active Learning	CodeCode Available	5

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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