News:

• [15. 12. 2021] Papers published during autumn:
  
  • J. Kalina, P. Vidnerová, J. Tichavský: A Comparison of Trend Estimators under Heteroscedasticity.
    In Artificial Intelligence and Soft Computing. ICAISC 2021 Proceedings, Part I. Cham: Springer, 2021. Lecture Notes in Artificial Intelligence, 12854. I
  • J. Kalina, P. Vidnerová: On kernel-based nonlinear regression estimation.
    In The 15th International Days of Statistics and Economics Conference Proceedings. Slaný: Melandrium, 2021.
  • P. Vidnerová, R. Neruda: Air Pollution Modelling by Machine Learning Methods.
    In Modelling (2021), pp. 659-674. E-ISSN 2673-3951


• [4. 11. 2021] Presentation at RELIK 2021
  J. Kalina, P. Vidnerová Application Of Implicitly Weighted Regression Quantiles: Analysis Of The 2018 Czech Presidential Election.


• [30. 9. 2021] New release of model M available on Github. Model-M v1.0


• [26. 9. 2021] Presentation at ITAT 2021 conference
  P. Vidnerová et al. Simulation of non-pharmaceutical interventions in an agent based epidemic model


• Preprint Importance of epidemic severity and vaccine mode of action and availability for delaying the second vaccine dose
  Luděk Berec, René Levínský, Jakub Weiner, Martin Šmíd, Roman Neruda, Petra Vidnerová, Gabriela Suchopárová
  doi: 10.1101/2021.06.30.21259752


• Preprint Rotation-based schedules in elementary schools to prevent COVID-19 spread: A simulation study
  Cyril Brom, Tomáš Diviák, Jakub Drbohlav, Václav Korbel, René Levínský, Roman Neruda, Gabriela Suchopárová, Josef Šlerka, Martin Šmíd, Jan Trnka, Petra Vidnerová
  doi: 10.1101/2021.06.28.21259628


• Presentation at IWFOS 2021
  J. Kalina, P. Vidnerová On Robust Training of Regression Neural Networks (poster), (slides)


• Presentation at ICAISC 2021
  J. Kalina, P. Vidnerová, J. Tichavský A Comparison of Trend Estimators under Heteroscedasticity


• Konference Poučení z pandemie COVID-19 (16. 6. 2021)
  Prezentace Simulace epidemiologických opatření v modelu M (P. Vidnerová, G. Suchopárová, R. Neruda)


• Preprint Model-M: An agent-based epidemic model of a middle-sized municipality
  Ludek Berec, Tomas Diviak, Ales Kubena, Rene Levinsky, Roman Neruda, Gabriela Suchoparova, Josef Slerka, Martin Smid, Jan Trnka, Vit Tucek, Petra Vidnerova, Milan Zajiceak, Frantisek Zapletal
  doi: 10.1101/2021.05.13.21257139


• Paper: J. Kalina, A. Neoral, P. Vidnerová: Effective Automatic Method Selection for Nonlinear Regression Modeling.
  Published in International Journal of Neural Systems .


• Článek M. Šmída: Spočítat to Covidu (Vesmír, duben 2021)


• Konference Covid v modelech (13. 11. 2020)


• Conference papers:
  • P. Vidnerová, Š. Procházka, R. Neruda: Multiobjective evolution for convolutional neural network architecture search,
    Springer, conference ICAISC 2020.
  • P. Vidnerová, R. Neruda: Multi-objective Evolution for Deep Neural Network Architecture Search.,
    Spinger, Neural Information Processing. ICONIP 2020.


• Preprint L. Berec at al The COVID-19 epidemic in the Czech Republic: retrospective analysis of measures (not) implemented during the spring first wave


• Presentation at ICAISC 2020
  P. Vidnerová, Š. Procházka, R. Neruda: Multiobjective evolution for convolutional neural network architecture search


• Presentation at The 14th International Days of Statistics and Economics:
  P. Vidnerová, J. Kalina: Least Weighted Absolute Value Estimator with an Application to Investment Data


• Several conference papers published:
  
  • J. Kalina, P. Vidnerová: A Metalearning Study for Robust Nonlinear Regression.
    Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. Springer, 2020
    https://link.springer.com/chapter/10.1007%2F978-3-030-48791-1_39
    
  
  
  • J. Kalina, P. Vidnerová: On Robust Training of Regression Neural Networks.
    Functional and High-Dimensional Statistics and Related Fields. Springer, 2020. IWFOS conference.
    http://hdl.handle.net/11104/0309464
    
  
  
  • J. Kalina, P. Vidnerová: Robust Multilayer Perceptrons: Robust Loss Functions and Their Derivatives.
    Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. Springer, 2020
    https://link.springer.com/chapter/10.1007%2F978-3-030-48791-1_43
    
  
  
  • P. Vidnerová, J. Kalina, Y. Güney: A Comparison of Robust Model Choice Criteria Within a Metalearning Study.
    AMISTAT 2019, Liberec. Analytical Methods in Statistics, Springer, 2020.
    http://hdl.handle.net/11104/0310207
    
  
  
• Presentation at EANN 2020 conference :
  A Metalearning Study for Robust Nonlinear Regression, J. Kalina, P. Vidnerová


• Conference paper: J. Kalina, P. Vidnerová: Regression Neural Networks with a Highly Robust Loss Function.
  AMISTAT 2019, Liberec. Springer, 2020.
  hdl.handle.net/11104/0306871


• Iniciativa Model antiCOVID-19 pro ČR
  Tisková zpráva z 20. 4.,
  Změmy chování české populace v době COVID-19 a jejich reflexe v epidemiologických modelech(CERGE-EI, 19. 5. 2020),
  konference NZIS a ISIN Open 2020(ÚZIS, 27.5. 2020)


• Paper: P. Vidnerová, R. Neruda: Vulnerability of classifiers to evolutionary generated adversarial examples
  Published in Neural Networks .
  Vulnerability of classifiers to evolutionary generated adversarial examples. Petra Vidnerová, Roman Neruda. Neural Networks, Volume 127, July 2020, Pages 168-181.


• Presentation at a workshop in Nová Seninka: From perceptron to deep neural networks.


• Presentation at our institute's open day (in Czech): Umělá inteligence: dobrý sluha, zlý pán?


• RBF Layer updated for Tensorflow 2.0. (tf.keras in fact) I moved the code to the new repository .


• Conference paper: J. Kalina, P. Vidnerová: Implicitly weighted robust estimation of quantiles in linear regression.
  37th International Conference on Mathematical Methods in Economics 2019. České Budějovice.
  hdl.handle.net/11104/0300322


• Presentation at AMISTAT conference :
  Metalearning for Robust Regression: Sensitivity and Robustification Petra Vidnerová, Jan Kalina, Aleš Neoral
  (book of abstracts)
  


• Jan Kalina's talk at Prague Stochastics 2019:
  Quantile Estimation in Neural Networks , Jan Kalina, Petra Vidnerová, Tomáš Jurica, Jan Tichavský, Nicole Tobišková


• Conference paper: J. Kalina, P. Vidnerová: Robust Training of Radial Basis Function Neural Networks
  Presented at ICAISC 2019 conference (poster).
  Kalina J., Vidnerová P. (2019) Robust Training of Radial Basis Function Neural Networks. In: Rutkowski L., Scherer R., Korytkowski M., Pedrycz W., Tadeusiewicz R., Zurada J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science, vol 11508. Springer, Cham


• Book review: Hitoshi Iba: Evolutionary approach to machine learning and deep neural networks: neuro-evolution and gene regulatory networks
  Vidnerová, P. Genetic Programming and Evolvable Machines (2019). https://doi.org/10.1007/s10710-019-09350-8