| 2025 | ICANN | Robotic Calibration Based on Haptic Feedback Improves Sim-to-Real Transfer. | Juraj Gavura, Michal Vavrecka, Igor Farkas, Connor Gde |
| 2025 | ICANN | Generating and Customizing Robotic Arm Trajectories Using Neural Networks. | Andrej Lcny, Matilde Antonj, Carlo Mazzola, Hana Hornckov, Igor Farkas |
| 2025 | IJCCI | Contrasting Human and Emergent Concepts in Image Classifiers. | Tamara Bla, Igor Farkas |
| 2024 | ICANN | Learning Low-Level Causal Relations Using a Simulated Robotic Arm. | Miroslav Cibula, Matthias Kerzel, Igor Farkas |
| 2024 | VISIGRAPP | RecViT: Enhancing Vision Transformer with Top-Down Information Flow. | Stefan Pcos, Iveta Beckov, Igor Farkas |
| 2023 | ICANN | Safe Reinforcement Learning in a Simulated Robotic Arm. | Luka Kovac, Igor Farkas |
| 2023 | ICANN | Robot at the Mirror: Learning to Imitate via Associating Self-supervised Models. | Andrej Lcny, Kristna Malinovsk, Igor Farkas |
| 2022 | ICANN | Examining the Proximity of Adversarial Examples to Class Manifolds in Deep Networks. | Stefan Pcos, Iveta Beckov, Igor Farkas |
| 2021 | ICANN | Advances in Adaptive Skill Acquisition. | Juraj Holas, Igor Farkas |
| 2021 | ICANN | Generative Properties of Universal Bidirectional Activation-Based Learning. | Kristna Malinovsk, Igor Farkas |
| 2021 | ICANN | Intrinsic Motivation Model Based on Reward Gating. | Matej Pechc, Igor Farkas |
| 2020 | ICANN | Computational Analysis of Robustness in Neural Network Classifiers. | Iveta Beckov, Stefan Pcos, Igor Farkas |
| 2020 | ICANN | Adaptive Skill Acquisition in Hierarchical Reinforcement Learning. | Juraj Holas, Igor Farkas |
| 2019 | ICANN | Embedding Complexity of Learned Representations in Neural Networks. | Tomas Kuzma, Igor Farkas |
| 2019 | IWANN | Echo State Networks with Artificial Astrocytes and Hebbian Connections. | Peter Gergel, Igor Farkas |
| 2018 | ICANN | Investigating the Role of Astrocyte Units in a Feedforward Neural Network. | Peter Gergel, Igor Farkas |
| 2018 | ICANN | Towards More Biologically Plausible Error-Driven Learning for Artificial Neural Networks. | Kristna Malinovsk, Ludovt Malinovsk, Igor Farkas |
| 2018 | IJCNN | Computational Analysis of Learned Representations in Deep Neural Network Classifiers. | Tomas Kuzma, Igor Farkas |
| 2018 | IJCNN | Evaluation of Information-Theoretic Measures in Echo State Networks on the Edge of Stability. | Miloslav Torda, Igor Farkas |
| 2017 | IJCNN | Maximizing memory capacity of echo state networks with orthogonalized reservoirs. | Igor Farkas, Peter Gergel |
| 2015 | IJCNN | Computational analysis of the Bidirectional Activation-based Learning in autoencoder task. | Peter Csiba, Igor Farkas |
| 2014 | CogSci | Calculation of object position in various reference frames with a robotic simulator. | Marcel Svec, Igor Farkas |
| 2014 | ICANN | Memory Capacity of Input-Driven Echo State Networks at the Edge of Chaos. | Peter Barancok, Igor Farkas |
| 2013 | ICANN | Bidirectional Activation-based Neural Network Learning Algorithm. | Igor Farkas, Kristna Rebrov |
| 2011 | CogSci | Modeling Utterance-mediated Attention in Situated Language Comprehension. | Jan Svantner, Igor Farkas, Matthew W. Crocker |
| 2011 | ICONIP | Bio-inspired Model of Spatial Cognition. | Michal Vavrecka, Igor Farkas, Lenka Lhotsk |
| 2009 | IJCCI | Recursive Self-organizing Networks for Processing Tree Structures - Empirical Comparison. | Pavol Vanco, Igor Farkas |
| 2008 | ICONIP | Learning Nonadjacent Dependencies with a Recurrent Neural Network. | Igor Farkas |
| 2007 | ESANN | Systematicity in sentence processing with a recursive self-organizing neural network. | Igor Farkas, Matthew W. Crocker |
| 2005 | ICNC | On Non-markovian Topographic Organization of Receptive Fields in Recursive Self-organizing Map. | Peter Tio, Igor Farkas |
| 2005 | IDEAL | Recursive Self-organizing Map as a Contractive Iterative Function System. | Peter Tio, Igor Farkas, Jort van Mourik |