Active Subsampling Using Deep Generative Models by Maximizing Expected Information Gain-Reference-Cited by-同舟云学术

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Active Subsampling Using Deep Generative Models by Maximizing Expected Information Gain

Published:2023-06-04 Issue: Volume: Page:
ISSN:
Container-title:ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

van de Camp Koen C.E.¹,Joudeh Hamdi²,Antunes Duarte J.¹,van Sloun Ruud J. G.²

Affiliation:

1. Eindhoven University of Technology,Department of Mechanical Engineering

2. Eindhoven University of Technology,Department of Electical Engineering

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10094559/10094560/10097104.pdf?arnumber=10097104

Reference21 articles.

1. Auto-encoding variational bayes;kingma,2013

2. Gradient-based learning applied to document recognition

3. Bayesian experimental design and shannon information;sebastiani;Proceedings of the Section on Bayesian Statistical Science,1997

4. Generative modeling by estimating gradients of the data distribution;song;Advances in neural information processing systems,2019

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