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 1691–1700 of 3073 papers

Title	Date	Tasks	Status	Hype
An Information Retrieval Approach to Building Datasets for Hate Speech Detection	Jun 17, 2021	Active LearningExplanation Generation	CodeCode Available	0
Gone Fishing: Neural Active Learning with Fisher Embeddings	Jun 17, 2021	Active Learning	CodeCode Available	1
Effective Evaluation of Deep Active Learning on Image Classification Tasks	Jun 16, 2021	Active LearningBenchmarking	—Unverified	0
A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams	Jun 16, 2021	Active LearningBenchmarking	—Unverified	0
Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq data	Jun 15, 2021	Active Learningfeature selection	—Unverified	0
Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit	Jun 14, 2021	Active LearningBinary Classification	—Unverified	0
Active Learning for Network Traffic Classification: A Technical Study	Jun 13, 2021	Active LearningClassification	—Unverified	0
Semi-supervised Active Regression	Jun 12, 2021	Active Learningregression	—Unverified	0
Rare event estimation using stochastic spectral embedding	Jun 9, 2021	Active Learning	—Unverified	0
Coresets for Classification -- Simplified and Strengthened	Jun 8, 2021	Active LearningClassification	—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