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 611–620 of 3073 papers

Title	Date	Tasks	Status	Score
Buy Me That Look: An Approach for Recommending Similar Fashion Products	Aug 26, 2020	Active LearningArticles	CodeCode Available	5
Active Learning for Semantic Segmentation with Multi-class Label Query	Sep 17, 2023	Active LearningMultiple Instance Learning	CodeCode Available	5
Black-Box Batch Active Learning for Regression	Feb 17, 2023	Active Learningregression	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
Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection	Sep 12, 2023	Active LearningDomain Adaptation	CodeCode Available	5
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active Learning	Jan 16, 2024	16kActive 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
Constraining the Parameters of High-Dimensional Models with Active Learning	May 19, 2019	Active LearningAstronomy	CodeCode Available	5
Bayesian Semi-supervised Learning with Graph Gaussian Processes	Sep 12, 2018	Active LearningGaussian Processes	CodeCode Available	5
Active Learning amidst Logical Knowledge	Sep 26, 2017	Active LearningBIG-bench Machine 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