Shedding Light on Variational Autoencoders

Autor:	Rogerio Iope, Sergio F Novaes, Jose Cupertino Ruiz Vargas, Silvio Luiz Stanzani, Thiago Tomei, Raphael Cóbe
Rok vydání:	2018
Předmět:	Diffraction Wavelength Computer science business.industry Feature (machine learning) Deep neural networks Pattern recognition Artificial intelligence business Interference (wave propagation) Random variable Decoding methods
Zdroj:	CLEI
DOI:	10.1109/clei.2018.00043
Popis:	Deep neural networks provide the canvas to create models of millions of parameters to fit distributions involving an equally large number of random variables. The contribution of this study is twofold. First, we introduce a diffraction dataset containing computer-based simulations of a Young's interference experiment. Then, we demonstrate the adeptness of variational autoencoders to learn diffraction patterns and extract a latent feature that correlates with the physical wavelength.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c7736cd957a827be78f9710c5c7496b4 https://doi.org/10.1109/clei.2018.00043 Zobrazit plný text záznamu