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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 1521–1530 of 3073 papers

Title	Date	Tasks	Status	Hype
GeneDisco: A Benchmark for Experimental Design in Drug Discovery	Oct 22, 2021	Active LearningDrug Discovery	CodeCode Available	1
Single-Modal Entropy based Active Learning for Visual Question Answering	Oct 21, 2021	Active LearningQuestion Answering	—Unverified	0
Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias	Oct 20, 2021	Active LearningAttribute	CodeCode Available	0
On Label-Eﬀicient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers	Oct 19, 2021	Active LearningContinual Learning	—Unverified	0
A-Optimal Active Learning	Oct 18, 2021	Active LearningExperimental Design	CodeCode Available	0
Active Learning for Deep Visual Tracking	Oct 17, 2021	Active LearningDiversity	—Unverified	0
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue	Oct 16, 2021	Abstractive Text SummarizationActive Learning	—Unverified	0
On the Importance of Effectively Adapting Pretrained Language Models for Active Learning	Oct 16, 2021	Active Learning	—Unverified	0
Deep Active Learning by Leveraging Training Dynamics	Oct 16, 2021	Active Learning	—Unverified	0
Nuances in Margin Conditions Determine Gains in Active Learning	Oct 16, 2021	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