Finite-State Reber Automaton and the Recurrent Neural Networks Trained in Supervised and Unsupervised Manner. - Michal Cernanský; Lubica Benusková (ICANN, 2001)

Optical spectra of quantum dot aggregates in sub-wetting layer region. - Karel Král 001; P. Zdenek; Z. Khás; Michal Cernanský (Microelectron. J., 2003)

Approaches Based on Markovian Architectural Bias in Recurrent Neural Networks. - Matej Makula; Michal Cernanský; Lubica Benusková (SOFSEM, 2004)

Markovian architectural bias of recurrent neural networks. - Peter Tiño; Michal Cernanský; Lubica Benusková (IEEE Trans. Neural Networks, 2004)

Feed-forward echo state networks. - Michal Cernanský; Matej Makula (IJCNN, 2005)

Comparison of Echo State Networks with Simple Recurrent Networks and Variable-Length Markov Models on Symbolic Sequences. - Michal Cernanský; Peter Tiño (ICANN (1), 2007)

Organization of the state space of a simple recurrent network before and after training on recursive linguistic structures. - Michal Cernanský; Matej Makula; Lubica Benusková (Neural Networks, 2007)

Predictive Modeling with Echo State Networks. - Michal Cernanský; Peter Tiño (ICANN (1), 2008)

Training Recurrent Connectionist Models on Symbolic Time Series. - Michal Cernanský; Lubica Benusková (ICONIP (1), 2008)

Improving the State Space Organization of Untrained Recurrent Networks. - Michal Cernanský; Matej Makula; Lubica Benusková (ICONIP (1), 2008)

On using of random access machine simulators in teaching of theoretical computer science. - Daniela Chudá; Martin Nehéz; Michal Cernanský (CompSysTech, 2009)

Training Recurrent Neural Network Using Multistream Extended Kalman Filter on Multicore Processor and Cuda Enabled Graphic Processor Unit. - Michal Cernanský (ICANN (1), 2009)