Zobrazeno 1 - 10
of 130
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, Zlatniczki, Ádám, Stippinger, Marcell, Fabó, Dániel, Sólyom, András, Erőss, Loránd, Telcs, András, Somogyvári, Zoltán
Publikováno v:
In Chaos, Solitons and Fractals: the interdisciplinary journal of Nonlinear Science, and Nonequilibrium and Complex Phenomena August 2024 185
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
Autor:
Benkő, Zsigmond, Zlatniczki, Ádám, Stippinger, Marcell, Fabó, Dániel, Sólyom, András, Erőss, Loránd, Telcs, András, Somogyvári, Zoltán
From ancient philosophers to modern economists, biologists, and other researchers, there has been a continuous effort to unveil causal relations. The most formidable challenge lies in deducing the nature of the causal relationship: whether it is unid
Externí odkaz:
http://arxiv.org/abs/1808.10806
Autor:
Tóth, Emília, Bokodi, Virág, Somogyvári, Zoltán, Maglóczky, Zsófia, Wittner, Lucia, Ulbert, István, Erőss, Loránd, Fabó, Dániel *
Publikováno v:
In Epilepsy Research January 2021 169