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pro vyhledávání: '"Gleyzer , Sergei"'
The next decade is expected to see a tenfold increase in the number of strong gravitational lenses, driven by new wide-field imaging surveys. To discover these rare objects, efficient automated detection methods need to be developed. In this work, we
Externí odkaz:
http://arxiv.org/abs/2410.01203
This study introduces a data-driven approach using machine learning (ML) techniques to explore and predict albedo anomalies on the Moon's surface. The research leverages diverse planetary datasets, including high-spatial-resolution albedo maps and el
Externí odkaz:
http://arxiv.org/abs/2407.05832
Recent work has established an alternative to traditional multi-layer perceptron neural networks in the form of Kolmogorov-Arnold Networks (KAN). The general KAN framework uses learnable activation functions on the edges of the computational graph fo
Externí odkaz:
http://arxiv.org/abs/2407.04149
Gravitational lensing data is frequently collected at low resolution due to instrumental limitations and observing conditions. Machine learning-based super-resolution techniques offer a method to enhance the resolution of these images, enabling more
Externí odkaz:
http://arxiv.org/abs/2406.08442
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
Deep learning techniques have been proven to provide excellent performance for a variety of high-energy physics applications, such as particle identification, event reconstruction and trigger operations. Recently, we developed an end-to-end deep lear
Externí odkaz:
http://arxiv.org/abs/2309.14254
Publikováno v:
Mach. Learn.: Sci. Technol. 5 025019 (2024)
Accurate observation of two or more particles within a very narrow time window has always been a challenge in modern physics. It creates the possibility of correlation experiments, such as the ground-breaking Hanbury Brown-Twiss experiment, leading t
Externí odkaz:
http://arxiv.org/abs/2306.09359