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 111–120 of 3073 papers

Title	Date	Tasks	Status	Hype
Active Anomaly Detection via Ensembles	Sep 17, 2018	Active LearningAnomaly Detection	CodeCode Available	1
BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation	Mar 4, 2022	Active Learning	CodeCode Available	1
Active Invariant Causal Prediction: Experiment Selection through Stability	Jun 10, 2020	Active LearningPrediction	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-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data	Nov 3, 2021	Active Learning	CodeCode Available	1
Causal-Guided Active Learning for Debiasing Large Language Models	Aug 23, 2024	Active LearningDiversity	CodeCode Available	1
CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Output	Jun 26, 2020	Active LearningDiagnostic	CodeCode Available	1
CitySurfaces: City-Scale Semantic Segmentation of Sidewalk Materials	Jan 6, 2022	Active LearningManagement	CodeCode Available	1
A Benchmark on Uncertainty Quantification for Deep Learning Prognostics	Feb 9, 2023	Active LearningDecision Making	CodeCode Available	1
Active Learning for Open-set Annotation	Jan 18, 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