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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 211–220 of 3073 papers

Title	Date	Tasks	Status	Hype
Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures	Feb 27, 2020	Active LearningClassification	CodeCode Available	1
Finding active galactic nuclei through Fink	Nov 20, 2022	Active LearningBinary Classification	CodeCode Available	1
Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings	Oct 16, 2020	Active LearningClustering	CodeCode Available	1
Fink: early supernovae Ia classification using active learning	Nov 22, 2021	Active LearningClassification	CodeCode Available	1
Active Learning for Optimal Intervention Design in Causal Models	Sep 10, 2022	Active LearningExperimental Design	CodeCode Available	1
GeneDisco: A Benchmark for Experimental Design in Drug Discovery	Oct 22, 2021	Active LearningDrug Discovery	CodeCode Available	1
Generating π-Functional Molecules Using STGG+ with Active Learning	Feb 20, 2025	Active Learningreinforcement-learning	CodeCode Available	1
Active Learning for Open-set Annotation	Jan 18, 2022	Active Learning	CodeCode Available	1
Active Learning for Deep Object Detection via Probabilistic Modeling	Mar 30, 2021	Active LearningClassification	CodeCode Available	1
Active Learning from the Web	Oct 15, 2022	Active LearningInformation Retrieval	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