Shedding Light on Variational Autoencoders

被引：1

作者：

Ruiz Vargas, J. C. ^{[1
]}

Novaes, S. F. ^{[1
]}

Cobe, R. ^{[1
]}

Iope, R. ^{[1
]}

Stanzani, S. ^{[1
]}

Tomei, T. R. ^{[1
]}

机构：

[1] Sao Paulo State Univ Unesp, Ctr Sci Comp NCC, Sao Paulo, SP, Brazil

来源：

2018 XLIV LATIN AMERICAN COMPUTER CONFERENCE (CLEI 2018) | 2018年

基金：

巴西圣保罗研究基金会;

关键词：

Variational Autoencoders; Machine Learning; Tensorflow; Fresnel diffraction;

D O I：

10.1109/CLEI.2018.00043

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：294 / 298

页数：5

共 50 条

[1] Generating Transit Light Curves with Variational Autoencoders
Woodward, Douglas
Stevens, Elizabeth
Linstead, Erik
2019 IEEE INTERNATIONAL CONFERENCE ON SPACE MISSION CHALLENGES FOR INFORMATION TECHNOLOGY (SMC-IT 2019), 2019, : 24 - 32
[2] Mixture variational autoencoders
Jiang, Shuoran
Chen, Yarui
Yang, Jucheng
Zhang, Chuanlei
Zhao, Tingting
PATTERN RECOGNITION LETTERS, 2019, 128 : 263 - 269
[3] Subitizing with Variational Autoencoders
Wever, Rijnder
Runia, Tom F. H.
COMPUTER VISION - ECCV 2018 WORKSHOPS, PT III, 2019, 11131 : 617 - 627
[4] An Introduction to Variational Autoencoders
Kingma, Diederik P.
Welling, Max
FOUNDATIONS AND TRENDS IN MACHINE LEARNING, 2019, 12 (04): : 4 - 89
[5] Mixtures of Variational Autoencoders
Ye, Fei
Bors, Adrian G.
2020 TENTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA), 2020,
[6] Variational Laplace Autoencoders
Park, Yookoon
Kim, Chris Dongjoo
Kim, Gunhee
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 97, 2019, 97
[7] Diffusion Variational Autoencoders
Rey, Luis A. Perez
Menkovski, Vlado
Portegies, Jim
PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2020, : 2704 - 2710
[8] Ladder Variational Autoencoders
Sonderby, Casper Kaae
Raiko, Tapani
Maaloe, Lars
Sonderby, Soren Kaae
Winther, Ole
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016), 2016, 29
[9] Overdispersed Variational Autoencoders
Shah, Harshil
Barber, David
Botev, Aleksandar
2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2017, : 1109 - 1116
[10] Tree Variational Autoencoders
Manduchi, Laura
Vandenhirtz, Moritz
Ryser, Alain
Vogt, Julia E.
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,

← 1 2 3 4 5 →