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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 291–300 of 3073 papers

Title	Date	Tasks	Status	Hype
Biological Sequence Design with GFlowNets	Mar 2, 2022	Active LearningDiversity	CodeCode Available	1
Boosting Active Learning via Improving Test Performance	Dec 10, 2021	Active LearningElectron Tomography	CodeCode Available	1
Box-Level Active Detection	Mar 23, 2023	Active Learningobject-detection	CodeCode Available	1
Building a Scalable and Interpretable Bayesian Deep Learning Framework for Quality Control of Free Form Surfaces	Apr 7, 1994	Active LearningBenchmarking	CodeCode Available	1
Causal-Guided Active Learning for Debiasing Large Language Models	Aug 23, 2024	Active LearningDiversity	CodeCode Available	1
ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation	Sep 11, 2023	Active LearningDrug Discovery	CodeCode Available	1
CitySurfaces: City-Scale Semantic Segmentation of Sidewalk Materials	Jan 6, 2022	Active LearningManagement	CodeCode Available	1
Class-Balanced Active Learning for Image Classification	Oct 9, 2021	Active LearningClassification	CodeCode Available	1
Active Anomaly Detection via Ensembles	Sep 17, 2018	Active LearningAnomaly Detection	CodeCode Available	1
Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression	Nov 16, 2022	Active Learning	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