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 301–310 of 3073 papers

Title	Date	Tasks	Status	Hype	Score
Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene	Aug 26, 2020	Active Learning	CodeCode Available	1	5
Efficient Bayesian Updates for Deep Learning via Laplace Approximations	Oct 12, 2022	Active LearningDeep Learning	CodeCode Available	1	5
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning	Jan 23, 2019	Active LearningAnomaly Detection	CodeCode Available	1	5
A deep active learning system for species identification and counting in camera trap images	Oct 22, 2019	Active LearningDecision Making	CodeCode Available	1	5
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks	Jan 27, 2021	Active LearningUncertainty Quantification	CodeCode Available	1	5
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages	Nov 7, 2022	Active LearningLanguage Modeling	CodeCode Available	1	5
GFlowNet Foundations	Nov 17, 2021	Active Learning	CodeCode Available	1	5
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments	Apr 5, 2023	Active LearningRecommendation Systems	CodeCode Available	1	5
A Framework and Benchmark for Deep Batch Active Learning for Regression	Mar 17, 2022	Active Learningregression	CodeCode Available	1	5
MoBYv2AL: Self-supervised Active Learning for Image Classification	Jan 4, 2023	Active LearningClassification	CodeCode Available	1	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