Year | Student | Doctoral thesis title |
2023 | Matej Pecháč | Intrinsically motivated reinforcement learning |
2021 | Matúš Tuna | Label-efficient learning in artificial neural networks |
2021 | Juraj Holas | Adaptive skill acquisition in hierarchical reinforcement learning |
2019 | Peter Gergeľ | Biologically inspired modeling of artificial neuro-glial networks |
2019 | Viliam Dillinger | Abstract state space construction in hierarchical reinforcement learning |
2014 | Kristína Rebrová | Grounding the meaning in sensorimotor cognition: a connectionist approach |
2013 | Michal Malý | Reinforcement learning with abstraction |
2012 | Vladimír Chudý | Systém pre rozpoznávanie slov a verifikáciu hovoriaceho |
2011 | Ján Švantner | Natural language processing with recurrent neural networks |
2010 | Pavol Vančo | Processing of tree-structured data with recursive self-organizing maps |