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 1881–1890 of 3073 papers

Title	Date	Tasks	Status	Hype
Active Hierarchical Imitation and Reinforcement Learning	Dec 14, 2020	Active LearningImitation Learning	—Unverified	0
LSCALE: Latent Space Clustering-Based Active Learning for Node Classification	Dec 13, 2020	Active LearningClustering	CodeCode Available	0
Accelerating high-throughput virtual screening through molecular pool-based active learning	Dec 13, 2020	Active LearningBayesian Optimization	CodeCode Available	1
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes	Dec 12, 2020	Active LearningBinary Classification	CodeCode Available	1
Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions	Dec 9, 2020	Active Learningimage-classification	—Unverified	0
Cost-Based Budget Active Learning for Deep Learning	Dec 9, 2020	Active LearningDeep Learning	—Unverified	0
A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design	Dec 8, 2020	Active LearningBIG-bench Machine Learning	—Unverified	0
Active Learning: Problem Settings and Recent Developments	Dec 8, 2020	Active Learning	—Unverified	0
Enhanced spatio-temporal electric load forecasts using less data with active deep learning	Dec 8, 2020	Active LearningBIG-bench Machine Learning	CodeCode Available	1
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval	Dec 7, 2020	Active LearningRetrieval	—Unverified	0

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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