Zobrazeno 1 - 10
of 9 453
pro vyhledávání: '"Roy, T. A."'
Autor:
Neto, Jogi Suda, Forestano, Roy T., Gleyzer, Sergei, Kong, Kyoungchul, Matchev, Konstantin T., Matcheva, Katia
Discovering new phenomena at the Large Hadron Collider (LHC) involves the identification of rare signals over conventional backgrounds. Thus binary classification tasks are ubiquitous in analyses of the vast amounts of LHC data. We develop a Lie-Equi
Externí odkaz:
http://arxiv.org/abs/2411.15315
Autor:
Cara, Marçal Comajoan, Dahale, Gopal Ramesh, Dong, Zhongtian, Forestano, Roy T., Gleyzer, Sergei, Justice, Daniel, Kong, Kyoungchul, Magorsch, Tom, Matchev, Konstantin T., Matcheva, Katia, Unlu, Eyup B.
Publikováno v:
Axioms 2024, 13(5), 323
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:
http://arxiv.org/abs/2405.10284
Autor:
Unlu, Eyup B., Cara, Marçal Comajoan, Dahale, Gopal Ramesh, Dong, Zhongtian, Forestano, Roy T., Gleyzer, Sergei, Justice, Daniel, Kong, Kyoungchul, Magorsch, Tom, Matchev, Konstantin T., Matcheva, Katia
Publikováno v:
Axioms v. 13, no 3, (2024) 187
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:
http://arxiv.org/abs/2402.00776
Autor:
Dong, Zhongtian, Cara, Marçal Comajoan, Dahale, Gopal Ramesh, Forestano, Roy T., Gleyzer, Sergei, Justice, Daniel, Kong, Kyoungchul, Magorsch, Tom, Matchev, Konstantin T., Matcheva, Katia, Unlu, Eyup B.
Publikováno v:
Axioms 2024, 13 (3), 188
This paper presents a comprehensive comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNN) and Quantum Neural Networks (QNN), juxtaposed against their classical counterparts: Equivariant Neural Networks (ENN) and Deep N
Externí odkaz:
http://arxiv.org/abs/2311.18744
Autor:
Forestano, Roy T., Cara, Marçal Comajoan, Dahale, Gopal Ramesh, Dong, Zhongtian, Gleyzer, Sergei, Justice, Daniel, Kong, Kyoungchul, Magorsch, Tom, Matchev, Konstantin T., Matcheva, Katia, Unlu, Eyup B.
Publikováno v:
Axioms 13 (2024) 160
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:
http://arxiv.org/abs/2311.18672
We describe a machine-learning-based surrogate model for reproducing the Bayesian posterior distributions for exoplanet atmospheric parameters derived from transmission spectra of transiting planets with typical retrieval software such as TauRex. The
Externí odkaz:
http://arxiv.org/abs/2310.10521
Autor:
Forestano, Roy T., Matchev, Konstantin T., Matcheva, Katia, Roman, Alexander, Unlu, Eyup B., Verner, Sarunas
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:
http://arxiv.org/abs/2309.07860
The next generation of telescopes will yield a substantial increase in the availability of high-resolution spectroscopic data for thousands of exoplanets. The sheer volume of data and number of planets to be analyzed greatly motivate the development
Externí odkaz:
http://arxiv.org/abs/2308.07604
Autor:
Forestano, Roy T., Matchev, Konstantin T., Matcheva, Katia, Roman, Alexander, Unlu, Eyup B., Verner, Sarunas
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:
http://arxiv.org/abs/2307.04891
Autor:
Forestano, Roy T., Matchev, Konstantin T., Matcheva, Katia, Roman, Alexander, Unlu, Eyup B., Verner, Sarunas
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:
http://arxiv.org/abs/2302.05383