@inproceedings{3e9610d0b4bc4d078ca2055d9bf2b4b2,
title = "Symmetric Simplicial Neural Networks",
abstract = "Convolutional Neural Networks are capable of perform many complex tasks such as image classification. Recently morphological functions where introduced as a replacement of the first convolutional layers in any net, using their non-linearities to achieve better accuracy for classification Neural Networks, but in most cases the functions are fixed beforehand and can not be trained. We propose the use of Symmetric Simplicial algorithm that can be trained to perform many morphological computations and even more complex functions. We present the training of a certain topology that uses Symmetric Simplicials instead of morphological functions and the classification accuracy achieved during the training process.",
keywords = "Training, Neurons, Training data, Feature extraction, Topology, Task analysis, Biological neural networks, PWL, Symmetric Simplicial functions, Deep Neural Networks, Morphological Neural Networks",
author = "N. Rodriguez and P. Julian and M. Villemur",
note = "DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.; 2021 55th Annual Conference on Information Sciences and Systems (CISS) ; Conference date: 24-03-2021 Through 26-03-2021",
year = "2021",
month = mar,
day = "24",
doi = "10.1109/CISS50987.2021.9400270",
language = "English",
isbn = "9781665412681",
series = "2021 55th Annual Conference on Information Sciences and Systems, CISS 2021",
publisher = "IEEE Computer Society",
pages = "1--6",
booktitle = "2021 55th Annual Conference on Information Sciences and Systems, CISS 2021",
address = "United States",
}