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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 2691–2700 of 3073 papers

Title	Date	Tasks	Status	Hype
Learning how to Active Learn: A Deep Reinforcement Learning Approach	Aug 8, 2017	Active LearningDeep Reinforcement Learning	CodeCode Available	0
Efficient Nonmyopic Active Search	Aug 1, 2017	Active LearningDrug Discovery	—Unverified	0
Active Learning for Top-K Rank Aggregation from Noisy Comparisons	Aug 1, 2017	Active Learning	CodeCode Available	0
Active Heteroscedastic Regression	Aug 1, 2017	Active LearningBinary Classification	—Unverified	0
VOILA: An Optimised Dialogue System for Interactively Learning Visually-Grounded Word Meanings (Demonstration System)	Aug 1, 2017	Active Learning	—Unverified	0
Proactive Learning for Named Entity Recognition	Aug 1, 2017	Active Learningnamed-entity-recognition	—Unverified	0
Active Learning for Convolutional Neural Networks: A Core-Set Approach	Aug 1, 2017	Active Learningimage-classification	CodeCode Available	1
Learning Algorithms for Active Learning	Jul 31, 2017	Active LearningPrediction	—Unverified	0
Interpretable Active Learning	Jul 31, 2017	Active Learning	CodeCode Available	0
A Nearly Instance Optimal Algorithm for Top-k Ranking under the Multinomial Logit Model	Jul 25, 2017	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