Zobrazeno 1 - 10
of 134
pro vyhledávání: '"Somogyvári Zoltán"'
Understanding causal relationships within a system is crucial for uncovering its underlying mechanisms. Causal discovery methods, which facilitate the construction of such models from time-series data, hold the potential to significantly advance scie
Externí odkaz:
http://arxiv.org/abs/2407.20694
Autor:
Hanczár, Gergely, Stippinger, Marcell, Hanák, Dávid, Kurbucz, Marcell T., Törteli, Olivér M., Chripkó, Ágnes, Somogyvári, Zoltán
In recent years, numerous screening methods have been published for ultrahigh-dimensional data that contain hundreds of thousands of features; however, most of these features cannot handle data with thousands of classes. Prediction models built to au
Externí odkaz:
http://arxiv.org/abs/2305.15793
Autor:
Stippinger, Marcell, Hanák, Dávid, Kurbucz, Marcell T., Hanczár, Gergely, Törteli, Olivér M., Somogyvári, Zoltán
Publikováno v:
SoftwareX 22 (2023) 101366
The lack of freely available (real-life or synthetic) high or ultra-high dimensional, multi-class datasets may hamper the rapidly growing research on feature screening, especially in the field of biometrics, where the usage of such datasets is common
Externí odkaz:
http://arxiv.org/abs/2206.10747
Autor:
Benkő, Zsigmond a, 1, Zlatniczki, Ádám a, b, h, 1, Stippinger, Marcell a, 1, Fabó, Dániel c, d, Sólyom, András c, Erőss, Loránd e, Telcs, András a, f, 2, Somogyvári, Zoltán a, g, ⁎, 2
Publikováno v:
In Chaos, Solitons and Fractals: the interdisciplinary journal of Nonlinear Science, and Nonequilibrium and Complex Phenomena August 2024 185
Publikováno v:
BMC Neuroscience, Vol 10, Iss Suppl 1, p P302 (2009)
Externí odkaz:
https://doaj.org/article/b3daddec096f48af97d67858d513a41f
Publikováno v:
BMC Neuroscience, Vol 10, Iss Suppl 1, p P37 (2009)
Externí odkaz:
https://doaj.org/article/76ab97b6c1034429957c22d271acdb6a
Autor:
Benkő, Zsigmond, Somogyvári, Zoltán
Determining hidden shared patterns behind dynamic phenomena can be a game-changer in multiple areas of research. Here we present the principles and show a method to identify hidden shared dynamics from time series by a two-module, feedforward neural
Externí odkaz:
http://arxiv.org/abs/2105.02322
Autor:
Benkő, Zsigmond, Stippinger, Marcell, Rehus, Roberta, Bencze, Attila, Fabó, Dániel, Hajnal, Boglárka, Erőss, Loránd, Telcs, András, Somogyvári, Zoltán
Data dimensionality informs us about data complexity and sets limit on the structure of successful signal processing pipelines. In this work we revisit and improve the manifold-adaptive Farahmand-Szepesv\'ari-Audibert (FSA) dimension estimator, makin
Externí odkaz:
http://arxiv.org/abs/2008.03221
Recognition of anomalous events is a challenging but critical task in many scientific and industrial fields, especially when the properties of anomalies are unknown. In this paper, we introduce a new anomaly concept called "unicorn" or unique event a
Externí odkaz:
http://arxiv.org/abs/2004.11468
Autor:
Stippinger, Marcell, Varga, Bálint, Benkő, Zsigmond, Fabó, Dániel, Erőss, Loránd, Somogyvári, Zoltán, Telcs, András
Publikováno v:
In Chaos, Solitons and Fractals: the interdisciplinary journal of Nonlinear Science, and Nonequilibrium and Complex Phenomena November 2023 176