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 251–260 of 3073 papers

Title	Date	Tasks	Status	Hype
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
Enhanced spatio-temporal electric load forecasts using less data with active deep learning	Dec 8, 2020	Active LearningBIG-bench Machine Learning	CodeCode Available	1
What Makes a "Good" Data Augmentation in Knowledge Distillation -- A Statistical Perspective	Dec 5, 2020	Active LearningData Augmentation	CodeCode Available	1
Counting People by Estimating People Flows	Dec 1, 2020	Active LearningCrowd Counting	CodeCode Available	1
Uncertainty estimation for molecular dynamics and sampling	Nov 10, 2020	Active LearningBIG-bench Machine Learning	CodeCode Available	1
Active Learning for BERT: An Empirical Study	Nov 1, 2020	Active LearningBinary text classification	CodeCode Available	1
Active Learning for Human-in-the-Loop Customs Inspection	Oct 27, 2020	Active LearningFraud Detection	CodeCode Available	1
Cold-start Active Learning through Self-supervised Language Modeling	Oct 19, 2020	Active LearningClassification	CodeCode Available	1
Semi-supervised Batch Active Learning via Bilevel Optimization	Oct 19, 2020	Active LearningBilevel Optimization	CodeCode Available	1
DEAL: Difficulty-aware Active Learning for Semantic Segmentation	Oct 17, 2020	Active LearningSegmentation	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