Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Dimitrios Kalatzis"'
Autor:
Konstantinos Zygogiannis, Eleni Pappa, Spyridon I Antonopoulos, Georgios Tsalimas, Konstantinos Manolakos, Ioannis Chatzikomninos, Savvas Moschos, Georgios C Thivaios, Dimitrios Kalatzis, Anastasios Kalampokis
Publikováno v:
Cureus.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f90e5ad4434d8ed5cbae92233825a72f
https://doi.org/10.7759/cureus.34494
https://doi.org/10.7759/cureus.34494
Autor:
Georgios Arvanitidis, Miguel González-Duque, Alison Pouplin, Dimitrios Kalatzis, Søren Hauberg
Publikováno v:
Arvanitidis, G, González-Duque, M, Pouplin, A, Kalatzis, D & Hauberg, S 2022, Pulling back information geometry . in Proceedings of the 25 th International Conference on Artificial Intelligence and Statistics . Proceedings of Machine Learning Research, vol. 151, 25 th International Conference on Artificial Intelligence and Statistics, 28/03/2022 .
Technical University of Denmark Orbit
Technical University of Denmark Orbit
Latent space geometry has shown itself to provide a rich and rigorous framework for interacting with the latent variables of deep generative models. The existing theory, however, relies on the decoder being a Gaussian distribution as its simple repar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af5d99abe516f63635ed4429091e0b36
https://orbit.dtu.dk/en/publications/804b50ed-a6f5-47d2-ab1e-e8d827724621
https://orbit.dtu.dk/en/publications/804b50ed-a6f5-47d2-ab1e-e8d827724621
Publikováno v:
Technical University of Denmark Orbit
Kalatzis, D, Eklund, D, Arvanitidis, G & Hauberg, S 2020, Variational Autoencoders with Riemannian Brownian Motion Priors . in H Daume & A Singh (eds), Proceedings of the 37 th International Conference on Machine Learning . vol. 119, International Machine Learning Society (IMLS), pp. 5020-5033, 37 th International Conference on Machine Learning, Virtual, Online, 13/07/2020 .
Proceedings of the 37th International Conference on Machine Learning (ICML 2020)
Proceedings of Machine Learning Research (PMLR)
Kalatzis, D, Eklund, D, Arvanitidis, G & Hauberg, S 2020, Variational Autoencoders with Riemannian Brownian Motion Priors . in H Daume & A Singh (eds), Proceedings of the 37 th International Conference on Machine Learning . vol. 119, International Machine Learning Society (IMLS), pp. 5020-5033, 37 th International Conference on Machine Learning, Virtual, Online, 13/07/2020 .
Proceedings of the 37th International Conference on Machine Learning (ICML 2020)
Proceedings of Machine Learning Research (PMLR)
Variational Autoencoders (VAEs) represent the given data in a low-dimensional latent space, which is generally assumed to be Euclidean. This assumption naturally leads to the common choice of a standard Gaussian prior over continuous latent variables
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a58c1d16851399a8c5c630b4cbe570ec
Autor:
Dimitrios Tzovaras, Dimitrios Giakoumis, Liming Chen, Joahannes Kroph, Erinc Merdivan, Matthieu Geist, Anastasios Vafeiadis, Raouf Hamzaoui, Dimitrios Kalatzis, Konstantinos Votis, Sten Hanke
Publikováno v:
2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Aug 2019, Leicester, United Kingdom. pp.144-149, ⟨10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00066⟩
SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI
2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Aug 2019, Leicester, United Kingdom. pp.144-149, ⟨10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00066⟩
SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI
International audience; We propose a new approach to text classificationin which we consider the input text as an image and apply2D Convolutional Neural Networks to learn the local andglobal semantics of the sentences from the variations of thevisual
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e929c750e1e3932de1f2467cd7b4d428
https://www.dora.dmu.ac.uk/handle/2086/18112
https://www.dora.dmu.ac.uk/handle/2086/18112