Zobrazeno 1 - 10
of 321
pro vyhledávání: '"Daniel Justice"'
Autor:
Johnson, Lonnell E.
Publikováno v:
CLA Journal, 1992 Mar 01. 35(3), 353-366.
Externí odkaz:
https://www.jstor.org/stable/44322499
Autor:
Marçal Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong, Roy T. Forestano, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu
Publikováno v:
Axioms, Vol 13, Iss 5, p 323 (2024)
We introduce a hybrid quantum-classical vision transformer architecture, notable for its integration of variational quantum circuits within both the attention mechanism and the multi-layer perceptrons. The research addresses the critical challenge of
Externí odkaz:
https://doaj.org/article/ba7655e110f347c69ac7cffb2b367b8c
Autor:
Zhongtian Dong, Marçal Comajoan Cara, Gopal Ramesh Dahale, Roy T. Forestano, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu
Publikováno v:
Axioms, Vol 13, Iss 3, p 188 (2024)
This paper presents a comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNNs) and Quantum Neural Networks (QNNs), juxtaposed against their classical counterparts: Equivariant Neural Networks (ENNs) and Deep Neural Netwo
Externí odkaz:
https://doaj.org/article/0192a5e664424331a27f91b6f3667e0a
Autor:
Eyup B. Unlu, Marçal Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong, Roy T. Forestano, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva
Publikováno v:
Axioms, Vol 13, Iss 3, p 187 (2024)
Models based on vision transformer architectures are considered state-of-the-art when it comes to image classification tasks. However, they require extensive computational resources both for training and deployment. The problem is exacerbated as the
Externí odkaz:
https://doaj.org/article/ce457c3cbddd493ca8ef48f9f78124d9
Autor:
Roy T. Forestano, Marçal Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu
Publikováno v:
Axioms, Vol 13, Iss 3, p 160 (2024)
Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structu
Externí odkaz:
https://doaj.org/article/e9db4f8727244fc289812cfefef0ace7
Autor:
Radcliff Jr., Dwight A.1 dradcliff@fuller.edu, Eshun, Daniel Justice2 D.Eshun@roehampton.ac.uk, Parsitau, Damaris3 dparsitau@yahoo.com
Publikováno v:
Mission Studies: Journal of the International Association for Mission Studies. 2021, Vol. 38 Issue 3, p315-318. 4p.
Autor:
Eshun, Daniel Justice1 D.Eshun@roehampton.ac.uk
Publikováno v:
Mission Studies: Journal of the International Association for Mission Studies. 2021, Vol. 38 Issue 3, p372-397. 26p.
Autor:
Daniel Justice Eshun
Publikováno v:
Mission Studies. 38:372-397
This paper (re)examines European missionary encounters with Ghanaians from the sixteenth – twenty-first centuries from Ghanaian perspectives. The paper makes three main arguments: first, European missionary endeavours were quite peripheral to ongoi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6cbbd1ed7ccfd4e9e258c84177d86d00
https://doi.org/10.1163/15733831-12341810
https://doi.org/10.1163/15733831-12341810
Publikováno v:
Mission Studies. 38:315-318
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d858e4355a98f0d49aae38ecdcf6de81
https://doi.org/10.1163/15733831-12341807
https://doi.org/10.1163/15733831-12341807
Publikováno v:
Quantum Science and Technology. 7:045014
The quantum approximate optimization algorithm (QAOA) is a hybrid quantum–classical algorithm to solve binary-variable optimization problems. Due to the short circuit depth and its expected robustness to systematic errors it is a promising candidat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::25faccdcc74c95d5e9c209930e8e80a0
https://doi.org/10.1088/2058-9565/ac6973
https://doi.org/10.1088/2058-9565/ac6973