A classification of C-Fuchsian subgroups of Picard modular groups
Parkkonen, J., & Paulin, F. (2017). A classification of C-Fuchsian subgroups of Picard modular groups. Mathematica Scandinavica, 121(1), 57-74. https://doi.org/10.7146/math.scand.a-26128
Published in
Mathematica ScandinavicaDate
2017Copyright
© Mathematica Scandinavica, 2017.
Publisher
Aarhus Universitet; Svenska MatematikersamfundetISSN Search the Publication Forum
0025-5521Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/27257764
Metadata
Show full item recordCollections
License
Related items
Showing items with similar title or keywords.
-
A classification of $\protect \mathbb{R}$-Fuchsian subgroups of Picard modular groups
Parkkonen, Jouni; Paulin, Frédéric (CEDERAM - Centre de diffusion de revues académiques mathématiques, 2018) -
The Truth is Out There : Focusing on Smaller to Guess Bigger in Image Classification
Terziyan, Vagan; Kaikova, Olena; Malyk, Diana; Branytskyi, Vladyslav (Elsevier, 2023)In Artificial Intelligence (AI) in general and in Machine Learning (ML) in particular, which are important and integral components of modern Industry 4.0, we often deal with uncertainty, e.g., lack of complete information ... -
A Computational Approach to Bio-optical Functional Group Classification of Phytoplankton in Inland Waters
Naik, Pritish; Pölönen, Ilkka; Salmi, Pauliina (Aalto-yliopisto, 2024) -
Neutrino interaction classification with a convolutional neural network in the DUNE far detector
DUNE Collaboration (American Physical Society, 2020)The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure CP-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on ... -
Comparison of feature importance measures as explanations for classification models
Saarela, Mirka; Jauhiainen, Susanne (Springer, 2021)Explainable artificial intelligence is an emerging research direction helping the user or developer of machine learning models understand why models behave the way they do. The most popular explanation technique is feature ...