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 2591–2600 of 3073 papers

Title	Date	Tasks	Status
Committee neural network potentials control generalization errors and enable active learning	Jun 2, 2020	Active LearningBIG-bench Machine Learning	CodeCode Available
ImitAL: Learned Active Learning Strategy on Synthetic Data	Aug 24, 2022	Active LearningInformativeness	CodeCode Available
ImitAL: Learning Active Learning Strategies from Synthetic Data	Aug 17, 2021	Active LearningImitation Learning	CodeCode Available
Active Gradual Machine Learning for Entity Resolution	Jan 16, 2022	Active LearningBIG-bench Machine Learning	CodeCode Available
Environmental Sensor Placement with Convolutional Gaussian Neural Processes	Nov 18, 2022	Active LearningGaussian Processes	CodeCode Available
Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary	May 1, 2017	Active LearningCross-Lingual Word Embeddings	CodeCode Available
The Battleship Approach to the Low Resource Entity Matching Problem	Nov 27, 2023	Active LearningData Integration	CodeCode Available
Active Collaborative Sensing for Energy Breakdown	Sep 2, 2019	Active Learning	CodeCode Available
Active Learning for Visual Question Answering: An Empirical Study	Nov 6, 2017	Active LearningVisual Question Answering	CodeCode Available
Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels	Dec 2, 2019	Active Learning	CodeCode Available

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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