RNDr. Petra Vidnerová, Ph.D.

Scientist

Department: Machine Learning
Phone: +420 266 05 3630
Office:
Email: (email)

Selected Publications

Deep Networks with RBF Layers to Prevent Adversarial Examples.

Vidnerová, Petra, Neruda, Roman.

, ,

In Artificial Intelligence and Soft Computing, pages 257-266 , , ISSN 0302-9743.

We propose a simple way to increase the robustness of deep neural network models to adversarial examples. The new architecture obtained by stacking deep neural network and RBF network is proposed. It is shown on experiments that such architecture is much more robust to adversarial examples than the original one while its accuracy on legitimate data stays more or less the same.

[SCOPUS] [DOI] [ASEP]

Learning Methods for Radial Basis Functions Networks.

Neruda, Roman, Kudová, Petra.

In Future Generation Computer Systems, volume 21, pages 1131-1142 , 2005, ISSN 0167-739X.

In this paper we present and examine several learning methods for RBF networks and their combinations. Performance of individual methods and their combinations is compared on experiments. The best results can be achieved by employing hybrid approaches that combine presented methods.

[WOS] [SCOPUS] [DOI] [ASEP]

Comparison of Behavior-based and Planning Techniques on the Small Robot Maze Exploration Problem.

Slušný, Stanislav, Neruda, Roman, Vidnerová, Petra.

In Neural Networks, volume 23, issue 4, pages 560-567 , 2010, ISSN 0893-6080.

A comparison of behavior-based and planning approaches of robot control is presented in this paper. We focus on miniature mobile robotic agents with limited sensory abilities. Two reactive control mechanisms for an agent are considered—a radial basis function neural network trained by evolutionary algorithm and a traditional reinforcement learning algorithm over a finite agent state space. The control architecture based on localization and planning is compared to the former method.

[WOS] [SCOPUS] 20346859 PUBMED [DOI] [ASEP]