Shedding light on variational autoencoders
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Data
2018-10-01
Autores
Ruiz Vargas, Jose Cupertino [UNESP]
Novaes, Sergio Ferraz [UNESP]
Cobe, Raphael [UNESP]
Iope, Rogerio [UNESP]
Stanzani, Silvio Luiz [UNESP]
Tomei, Thiago [UNESP]
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Resumo
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.
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Palavras-chave
Fresnel diffraction, Machine Learning, Tensorflow, Variational Autoencoders
Como citar
Proceedings - 2018 44th Latin American Computing Conference, CLEI 2018, p. 294-298.