Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Roy T. Forestano"'
Autor:
Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Alexander Roman, Eyup B. Unlu, Sarunas Verner
Publikováno v:
Physics Letters B, Vol 847, Iss , Pp 138306- (2023)
Deep learning was recently successfully used in deriving symmetry transformations that preserve important physics quantities. Being completely agnostic, these techniques postpone the identification of the discovered symmetries to a later stage. In th
Externí odkaz:
https://doaj.org/article/01b3b6569d594067ba851a0ec0c8c0c4
Autor:
Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Alexander Roman, Eyup B. Unlu, Sarunas Verner
Publikováno v:
Physics Letters B, Vol 847, Iss , Pp 138266- (2023)
Recent work has applied supervised deep learning to derive continuous symmetry transformations that preserve the data labels and to obtain the corresponding algebras of symmetry generators. This letter introduces two improved algorithms that signific
Externí odkaz:
https://doaj.org/article/a5b8dda612424f2fbcb937d8f2bc750e
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, Konstantin T. Matchev, Katia Matcheva, Alexander Roman, Eyup B. Unlu, Sarunas Verner
Publikováno v:
Physics Letters B, Vol 844, Iss , Pp 138086- (2023)
Recent work has used deep learning to derive symmetry transformations, which preserve conserved quantities, and to obtain the corresponding algebras of generators. In this letter, we extend this technique to derive sparse representations of arbitrary
Externí odkaz:
https://doaj.org/article/bb46654c087742c58bde546978fdae84
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
Publikováno v:
Symmetry, Vol 15, Iss 7, p 1352 (2023)
A fundamental task in data science is the discovery, description, and identification of any symmetries present in the data. We developed a deep learning methodology for the simultaneous discovery of multiple non-trivial continuous symmetries across a
Externí odkaz:
https://doaj.org/article/83310083b06a44328d3d2ebc186d27b9
Publikováno v:
The Astrophysical Journal, Vol 958, Iss 2, p 106 (2023)
The next generation of telescopes will yield a substantial increase in the availability of high-quality spectroscopic data for thousands of exoplanets. The sheer volume of data and number of planets to be analyzed greatly motivate the development of
Externí odkaz:
https://doaj.org/article/1e15a26982574b098f7077f1308e31ea
Publikováno v:
Symmetry; Volume 15; Issue 7; Pages: 1352
A fundamental task in data science is the discovery, description, and identification of any symmetries present in the data. We developed a deep learning methodology for the simultaneous discovery of multiple non-trivial continuous symmetries across a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::a2d9f7fad2e3f5d3cb89325e77653488