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 531–540 of 3073 papers

Title	Date	Tasks	Status	Score
Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning	Mar 18, 2022	Active Learning	CodeCode Available	5
Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression	Jul 31, 2023	Active LearningDiversity	CodeCode Available	5
Computational Assessment of Hyperpartisanship in News Titles	Jan 16, 2023	Active LearningLanguage Modelling	CodeCode Available	5
Active Learning-Based Species Range Estimation	Nov 3, 2023	Active Learning	CodeCode Available	5
Class Balance Matters to Active Class-Incremental Learning	Dec 9, 2024	Active Learningclass-incremental learning	CodeCode Available	5
Active Learning in CNNs via Expected Improvement Maximization	Nov 27, 2020	Active Learning	CodeCode Available	5
Active Collaborative Sensing for Energy Breakdown	Sep 2, 2019	Active Learning	CodeCode Available	5
Active Classification with Uncertainty Comparison Queries	Aug 3, 2020	Active LearningClassification	CodeCode Available	5
Active learning in annotating micro-blogs dealing with e-reputation	Jun 16, 2017	Active LearningInformation Retrieval	CodeCode Available	5
Characterizing the robustness of Bayesian adaptive experimental designs to active learning bias	May 27, 2022	Active LearningExperimental Design	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