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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 301–310 of 3073 papers

Title	Date	Tasks	Status	Hype
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes	Jun 18, 2019	Active LearningContinual Learning	CodeCode Available	1
Self-Supervised Exploration via Disagreement	Jun 10, 2019	Active LearningEfficient Exploration	CodeCode Available	1
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds	Jun 9, 2019	Active LearningDiversity	CodeCode Available	1
Learning Loss for Active Learning	May 9, 2019	Active Learningimage-classification	CodeCode Available	1
Variational Adversarial Active Learning	Mar 31, 2019	Active Learningimage-classification	CodeCode Available	1
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning	Jan 23, 2019	Active LearningAnomaly Detection	CodeCode Available	1
Prompsit's submission to WMT 2018 Parallel Corpus Filtering shared task	Oct 1, 2018	Active LearningLanguage Modeling	CodeCode Available	1
Active Anomaly Detection via Ensembles	Sep 17, 2018	Active LearningAnomaly Detection	CodeCode Available	1
OBOE: Collaborative Filtering for AutoML Model Selection	Aug 9, 2018	Active LearningAutoML	CodeCode Available	1
Bayesian Model-Agnostic Meta-Learning	Jun 11, 2018	Active Learningimage-classification	CodeCode Available	1

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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