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 2311–2320 of 3073 papers

Title	Date	Tasks	Status
Temporal Coherence for Active Learning in Videos	Aug 30, 2019	Active LearningAutonomous Driving	—Unverified
Active Learning for Domain Classification in a Commercial Spoken Personal Assistant	Aug 29, 2019	Active Learningdomain classification	—Unverified
O-MedAL: Online Active Deep Learning for Medical Image Analysis	Aug 28, 2019	Active LearningDeep Learning	CodeCode Available
A novel active learning-based Gaussian process metamodelling strategy for estimating the full probability distribution in forward UQ analysis	Aug 27, 2019	Active LearningComputational Efficiency	—Unverified
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers	Aug 23, 2019	Active LearningCross-Lingual Transfer	CodeCode Available
NE-LP: Normalized Entropy and Loss Prediction based Sampling for Active Learning in Chinese Word Segmentation on EHRs	Aug 22, 2019	Active LearningChinese Word Segmentation	—Unverified
SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs	Aug 22, 2019	Active LearningGraph Embedding	—Unverified
ED2: Two-stage Active Learning for Error Detection -- Technical Report	Aug 17, 2019	Active LearningClassification	—Unverified
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model	Aug 13, 2019	Active LearningBIG-bench Machine Learning	CodeCode Available
Supervised Negative Binomial Classifier for Probabilistic Record Linkage	Aug 11, 2019	Active Learning	—Unverified

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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