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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 561–570 of 3073 papers

Title	Date	Tasks	Status	Score
Active Learning Framework for Cost-Effective TCR-Epitope Binding Affinity Prediction	Oct 16, 2023	Active LearningPrediction	CodeCode Available	5
Class Balance Matters to Active Class-Incremental Learning	Dec 9, 2024	Active Learningclass-incremental learning	CodeCode Available	5
Active Learning for Visual Question Answering: An Empirical Study	Nov 6, 2017	Active LearningVisual Question Answering	CodeCode Available	5
Active Classification with Uncertainty Comparison Queries	Aug 3, 2020	Active LearningClassification	CodeCode Available	5
Bayesian Active Learning for Classification and Preference Learning	Dec 24, 2011	Active LearningClassification	CodeCode Available	5
Clinical Trial Active Learning	Jul 20, 2023	Active Learning	CodeCode Available	5
Detecting Minority Arguments for Mutual Understanding: A Moderation Tool for the Online Climate Change Debate	Oct 1, 2022	Active Learning	CodeCode Available	5
Active Learning for Top-K Rank Aggregation from Noisy Comparisons	Aug 1, 2017	Active Learning	CodeCode Available	5
Buy Me That Look: An Approach for Recommending Similar Fashion Products	Aug 26, 2020	Active LearningArticles	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

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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