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 121–130 of 3073 papers

Title	Date	Tasks	Status	Hype
Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression	Nov 16, 2022	Active Learning	CodeCode Available	1
LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation	Nov 11, 2022	Active LearningLIDAR Semantic Segmentation	CodeCode Available	1
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages	Nov 7, 2022	Active LearningLanguage Modeling	CodeCode Available	1
Materials Property Prediction with Uncertainty Quantification: A Benchmark Study	Nov 4, 2022	Active LearningBand Gap	CodeCode Available	1
Fine-Tuning Language Models via Epistemic Neural Networks	Nov 3, 2022	Active LearningLanguage Modeling	CodeCode Available	1
Fast and robust Bayesian Inference using Gaussian Processes with GPry	Nov 3, 2022	Active LearningBayesian Inference	CodeCode Available	1
cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule Diagnosis	Oct 28, 2022	Active LearningAttribute	CodeCode Available	1
Provable Safe Reinforcement Learning with Binary Feedback	Oct 26, 2022	Active Learningreinforcement-learning	CodeCode Available	1
Multi-Objective GFlowNets	Oct 23, 2022	Active LearningDiversity	CodeCode Available	1
Bayesian Optimization with Conformal Prediction Sets	Oct 22, 2022	Active LearningBayesian Optimization	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