Trainable Compound Activation Functions for Machine Learning-Reference-Cited by-同舟云学术

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Trainable Compound Activation Functions for Machine Learning

Published:2022-08-29 Issue: Volume: Page:
ISSN:
Container-title:2022 30th European Signal Processing Conference (EUSIPCO)
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Author:

Baggenstoss Paul M.¹

Affiliation:

1. Fraunhofer FKIE,Wachtberg,Germany,53343

Funder

Office of Naval Research Global

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9908519/9909515/09909774.pdf?arnumber=9909774

Reference19 articles.

1. Exponential family harmo-niums with an application to information retrieval;welling;Advances in neural information processing systems,2004

2. Evaluating the RBM without integration using PDF projection

3. New restricted Boltzmann machines and deep belief networks for audio classification;baggenstoss;2021 ITG Speech Communication Kiel (Virtual),2021

4. Rectified linear units improve restricted boltzmann machines;nair;Proceedings of the 27th International Conference on Machine Learning,2010

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Improved Auto-Encoding Using Deterministic Projected Belief Networks and Compound Activation Functions;2023 31st European Signal Processing Conference (EUSIPCO);2023-09-04

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