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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 1981–1990 of 3073 papers

Title	Date	Tasks	Status	Hype
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification	Sep 30, 2020	Active LearningData Augmentation	—Unverified	0
Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning	Sep 27, 2020	Active LearningBIG-bench Machine Learning	—Unverified	0
Multi-task Causal Learning with Gaussian Processes	Sep 27, 2020	Active LearningBayesian Optimization	CodeCode Available	1
Online Learning of Non-Markovian Reward Models	Sep 26, 2020	Active Learning	—Unverified	0
ALICE: Active Learning with Contrastive Natural Language Explanations	Sep 22, 2020	Active LearningClassification	—Unverified	0
Model-Centric and Data-Centric Aspects of Active Learning for Deep Neural Networks	Sep 22, 2020	Active LearningInformativeness	—Unverified	0
DISPATCH: Design Space Exploration of Cyber-Physical Systems	Sep 21, 2020	Active LearningBayesian Optimization	—Unverified	0
Active Learning for Product Type Ontology Enhancement in E-commerce	Sep 19, 2020	Active LearningVocal Bursts Type Prediction	—Unverified	0
Mean-Variance Analysis in Bayesian Optimization under Uncertainty	Sep 17, 2020	Active LearningBayesian Optimization	—Unverified	0
Beyond Accuracy: ROI-driven Data Analytics of Empirical Data	Sep 14, 2020	Active Learning	—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