Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Hybrid Helmholtz machines

Autor: Frank Phillipson, Niels M. P. Neumann, Teresa J. van Dam, Hans van den Berg

Publikováno v: Quantum Information Processing, 19(6)
Quantum Information Processing, 19(6):174. Springer
Quantum Information Processing, 19(6):174. Springer Verlag

Quantum machine learning has the potential to overcome problems that current classical machine learning algorithms face, such as large data requirements or long learning times. Sampling is one of the aspects of classical machine learning that might b

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bc43cdbefca9b2f58b5705ed0a94b2dc
https://cris.maastrichtuniversity.nl/en/publications/9406b358-87e8-4f78-bcf1-44233ce0fc23

A modified version of Helmholtz machine by using a Restricted Boltzmann Machine to model the generative probability of the top layer

Autor: Jiangshe Zhang, Chunxia Zhang, Junying Hu, Nannan Ji

Publikováno v: Neurocomputing. 267:1-17

The Helmholtz machine is an unsupervised deep neural network with different bottom-up recognition weights and top-down generative weights, which attempts to build probability density models of sensory inputs. The recognition weights are used to deter

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::536d318c2f1d263d247fd548e85d432c
https://doi.org/10.1016/j.neucom.2017.03.001

Capsule generative models

Autor: Yifeng Li, Xiaodan Zhu

Publikováno v: Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation ISBN: 9783030304867
ICANN (1)

Neuroscience studies inspire that structures are needed in the hidden space of deep learning models. In this paper, we propose a capsule restricted Boltzmann machine and a capsule Helmholtz machine by replacing individual hidden variables with encaps

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::003f39574bb089641222fb92ab358bb5
https://nrc-publications.canada.ca/eng/view/object/?id=64873d48-bac4-4d60-84b7-de040865b0e2

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.