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2020 – today
- 2024
[j12]Matej Pechác
, Michal Chovanec, Igor Farkas:
Self-supervised network distillation: An effective approach to exploration in sparse reward environments. Neurocomputing 599: 128033 (2024)- [c39]Miroslav Cibula, Matthias Kerzel, Igor Farkas:
Learning Low-Level Causal Relations Using a Simulated Robotic Arm. ICANN (10) 2024: 285-298 - [c38]Stefan Pócos, Iveta Becková, Igor Farkas:
RecViT: Enhancing Vision Transformer with Top-Down Information Flow. VISIGRAPP (3): VISAPP 2024: 749-756 - [i6]Miroslav Cibula, Matthias Kerzel, Igor Farkas:
Learning Low-Level Causal Relations using a Simulated Robotic Arm. CoRR abs/2410.07751 (2024) - 2023
- [c37]Andrej Lúcny, Kristína Malinovská, Igor Farkas:
Robot at the Mirror: Learning to Imitate via Associating Self-supervised Models. ICANN (1) 2023: 471-482 - [c36]Luka Kovac, Igor Farkas:
Safe Reinforcement Learning in a Simulated Robotic Arm. ICANN (1) 2023: 585-589 - [c35]Dmytro Herashchenko, Igor Farkas:
Appearance-Based Gaze Estimation Enhanced with Synthetic Images Using Deep Neural Networks. SSCI 2023: 129-134 - [i5]Matej Pechác, Michal Chovanec, Igor Farkas:
Exploration by self-supervised exploitation. CoRR abs/2302.11563 (2023) - [i4]Andrej Lúcny, Kristína Malinovská, Igor Farkas:
Robot at the Mirror: Learning to Imitate via Associating Self-supervised Models. CoRR abs/2311.13226 (2023) - [i3]Dmytro Herashchenko, Igor Farkas:
Appearance-based gaze estimation enhanced with synthetic images using deep neural networks. CoRR abs/2311.14175 (2023) - [i2]Luka Kovac, Igor Farkas:
Safe Reinforcement Learning in a Simulated Robotic Arm. CoRR abs/2312.09468 (2023) - 2022
- [c34]Stefan Pócos, Iveta Becková, Igor Farkas:
Examining the Proximity of Adversarial Examples to Class Manifolds in Deep Networks. ICANN (4) 2022: 645-656 - [c33]Kristína Malinovská, Igor Farkas, Jana Harvanová, Matej Hoffmann:
A connectionist model of associating proprioceptive and tactile modalities in a humanoid robot. ICDL 2022: 336-342 - [i1]Stefan Pócos, Iveta Becková, Igor Farkas:
Examining the Proximity of Adversarial Examples to Class Manifolds in Deep Networks. CoRR abs/2204.05764 (2022) - 2021
- [c32]Kristína Malinovská, Igor Farkas:
Generative Properties of Universal Bidirectional Activation-Based Learning. ICANN (3) 2021: 80-83 - [c31]Juraj Holas, Igor Farkas:
Advances in Adaptive Skill Acquisition. ICANN (4) 2021: 650-661 - [c30]Matej Pechác, Igor Farkas:
Intrinsic Motivation Model Based on Reward Gating. ICANN (4) 2021: 688-699 - [c29]Matús Tuna, Kristína Malinovská, Igor Farkas, Svatopluk Kraus, Pavel Krsek:
Semi-supervised Learning in Camera Surveillance Image Classification. ICCP 2021: 155-162 - [e7]Igor Farkas, Paolo Masulli, Sebastian Otte, Stefan Wermter:
Artificial Neural Networks and Machine Learning - ICANN 2021 - 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14-17, 2021, Proceedings, Part I. Lecture Notes in Computer Science 12891, Springer 2021, ISBN 978-3-030-86361-6 [contents] - [e6]Igor Farkas, Paolo Masulli, Sebastian Otte, Stefan Wermter:
Artificial Neural Networks and Machine Learning - ICANN 2021 - 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14-17, 2021, Proceedings, Part II. Lecture Notes in Computer Science 12892, Springer 2021, ISBN 978-3-030-86339-5 [contents] - [e5]Igor Farkas, Paolo Masulli, Sebastian Otte, Stefan Wermter:
Artificial Neural Networks and Machine Learning - ICANN 2021 - 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14-17, 2021, Proceedings, Part III. Lecture Notes in Computer Science 12893, Springer 2021, ISBN 978-3-030-86364-7 [contents] - [e4]Igor Farkas, Paolo Masulli, Sebastian Otte, Stefan Wermter:
Artificial Neural Networks and Machine Learning - ICANN 2021 - 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14-17, 2021, Proceedings, Part IV. Lecture Notes in Computer Science 12894, Springer 2021, ISBN 978-3-030-86379-1 [contents] - [e3]Igor Farkas, Paolo Masulli, Sebastian Otte, Stefan Wermter:
Artificial Neural Networks and Machine Learning - ICANN 2021 - 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14-17, 2021, Proceedings, Part V. Lecture Notes in Computer Science 12895, Springer 2021, ISBN 978-3-030-86382-1 [contents] - 2020
- [c28]Iveta Becková, Stefan Pócos, Igor Farkas:
Computational Analysis of Robustness in Neural Network Classifiers. ICANN (1) 2020: 65-76 - [c27]Juraj Holas, Igor Farkas:
Adaptive Skill Acquisition in Hierarchical Reinforcement Learning. ICANN (2) 2020: 383-394 - [c26]Stefan Pócos, Iveta Becková, Tomás Kuzma, Igor Farkas:
Assessment of Manifold Unfolding in Trained Deep Neural Network Classifiers. TAILOR 2020: 93-103 - [e2]Igor Farkas, Paolo Masulli, Stefan Wermter:
Artificial Neural Networks and Machine Learning - ICANN 2020 - 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020, Proceedings, Part I. Lecture Notes in Computer Science 12396, Springer 2020, ISBN 978-3-030-61608-3 [contents] - [e1]Igor Farkas, Paolo Masulli, Stefan Wermter:
Artificial Neural Networks and Machine Learning - ICANN 2020 - 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020, Proceedings, Part II. Lecture Notes in Computer Science 12397, Springer 2020, ISBN 978-3-030-61615-1 [contents]
2010 – 2019
- 2019
- [c25]Stefan Korecko, Branislav Sobota, Marián Hudák, Igor Farkas, Barbora Cimrová, Peter Vasil, Dominik Trojcák:
Experimental Procedure for Evaluation of Visuospatial Cognitive Functions Training in Virtual Reality. AISI 2019: 643-652 - [c24]Kristína Malinovská, Ludovít Malinovský, Pavel Krsek, Svatopluk Kraus, Igor Farkas:
UBAL: a Universal Bidirectional Activation-based Learning Rule for Neural Networks. CIIS 2019: 57-62 - [c23]Tomas Kuzma, Igor Farkas:
Embedding Complexity of Learned Representations in Neural Networks. ICANN (2) 2019: 518-528 - [c22]Jakub Pospíchal, Igor Farkas, Matej Pechác, Kristína Malinovská:
Modeling Self-organized Emergence of Perspective In/variant Mirror Neurons in a Robotic System. ICDL-EPIROB 2019: 278-283 - [c21]Peter Gergel, Igor Farkas:
Echo State Networks with Artificial Astrocytes and Hebbian Connections. IWANN (1) 2019: 457-466 - 2018
- [j11]Jan Jug, Tine Kolenik, André Ofner, Igor Farkas:
Computational model of enactive visuospatial mental imagery using saccadic perceptual actions. Cogn. Syst. Res. 49: 157-177 (2018) - [j10]Matej Hoffmann, Zdenek Straka, Igor Farkas, Michal Vavrecka, Giorgio Metta:
Robotic Homunculus: Learning of Artificial Skin Representation in a Humanoid Robot Motivated by Primary Somatosensory Cortex. IEEE Trans. Cogn. Dev. Syst. 10(2): 163-176 (2018) - [c20]Stefan Korecko, Marián Hudák, Branislav Sobota, Martin Marko, Barbora Cimrová, Igor Farkas, Roman Rosipal:
Assessment and training of visuospatial cognitive functions in virtual reality: proposal and perspective. CogInfoCom 2018: 39-44 - [c19]Peter Gergel, Igor Farkas:
Investigating the Role of Astrocyte Units in a Feedforward Neural Network. ICANN (3) 2018: 73-83 - [c18]Kristína Malinovská, Ludovít Malinovský, Igor Farkas:
Towards More Biologically Plausible Error-Driven Learning for Artificial Neural Networks. ICANN (3) 2018: 228-231 - [c17]Tomas Kuzma, Igor Farkas:
Computational Analysis of Learned Representations in Deep Neural Network Classifiers. IJCNN 2018: 1-8 - [c16]Miloslav Torda, Igor Farkas:
Evaluation of Information-Theoretic Measures in Echo State Networks on the Edge of Stability. IJCNN 2018: 1-6 - 2017
- [c15]Igor Farkas, Peter Gergel:
Maximizing memory capacity of echo state networks with orthogonalized reservoirs. IJCNN 2017: 2437-2442 - 2016
- [j9]Igor Farkas, Radomír Bosák, Peter Gergel:
Computational analysis of memory capacity in echo state networks. Neural Networks 83: 109-120 (2016) - 2015
- [c14]Peter Gergel, Igor Farkas:
Connectionist Modeling of Part-Whole Analogy Learning. EAPCogSci 2015 - [c13]Peter Csiba, Igor Farkas:
Computational analysis of the Bidirectional Activation-based Learning in autoencoder task. IJCNN 2015: 1-6 - 2014
- [j8]Michal Vavrecka, Igor Farkas:
A Multimodal Connectionist Architecture for Unsupervised Grounding of Spatial Language. Cogn. Comput. 6(1): 101-112 (2014) - [c12]Marcel Svec, Igor Farkas:
Calculation of object position in various reference frames with a robotic simulator. CogSci 2014 - [c11]Peter Barancok, Igor Farkas:
Memory Capacity of Input-Driven Echo State Networks at the Edge of Chaos. ICANN 2014: 41-48 - 2013
- [c10]Igor Farkas, Kristína Rebrová:
Bidirectional Activation-based Neural Network Learning Algorithm. ICANN 2013: 154-161 - [c9]Kristína Rebrová, Matej Pechác, Igor Farkas:
Towards a robotic model of the mirror neuron system. ICDL-EPIROB 2013: 1-6 - 2012
- [j7]Igor Farkas, Tomás Malík, Kristína Rebrová:
Grounding the Meanings in Sensorimotor Behavior using Reinforcement Learning. Frontiers Neurorobotics 6: 1 (2012) - 2011
- [c8]Jan Svantner, Igor Farkas, Matthew W. Crocker:
Modeling Utterance-mediated Attention in Situated Language Comprehension. CogSci 2011 - [c7]Michal Vavrecka, Igor Farkas, Lenka Lhotská:
Bio-inspired Model of Spatial Cognition. ICONIP (1) 2011: 443-450 - 2010
- [j6]Pavol Vanco, Igor Farkas:
Experimental comparison of recursive self-organizing maps for processing tree-structured data. Neurocomputing 73(7-9): 1362-1375 (2010)
2000 – 2009
- 2009
- [j5]Ivan Bajla, Frantisek Rublík, Barbora Arendacká, Igor Farkas, Klára Hornisová, Svorad Stolc, Viktor Witkovský:
Segmentation and supervised classification of image objects in Epo doping-control. Mach. Vis. Appl. 20(4): 243-259 (2009) - [c6]Pavol Vanco, Igor Farkas:
Recursive Self-organizing Networks for Processing Tree Structures - Empirical Comparison. IJCCI 2009: 459-466 - 2008
- [j4]Igor Farkas, Matthew W. Crocker:
Syntactic systematicity in sentence processing with a recurrent self-organizing network. Neurocomputing 71(7-9): 1172-1179 (2008) - [c5]Igor Farkas:
Learning Nonadjacent Dependencies with a Recurrent Neural Network. ICONIP (2) 2008: 292-299 - 2007
- [c4]Igor Farkas, Matthew W. Crocker:
Systematicity in sentence processing with a recursive self-organizing neural network. ESANN 2007: 49-54 - 2006
- [j3]Peter Tiño, Igor Farkas, Jort van Mourik:
Dynamics and Topographic Organization of Recursive Self-Organizing Maps. Neural Comput. 18(10): 2529-2567 (2006) - 2005
- [c3]Peter Tiño, Igor Farkas:
On Non-markovian Topographic Organization of Receptive Fields in Recursive Self-organizing Map. ICNC (2) 2005: 676-685 - [c2]Peter Tiño, Igor Farkas, Jort van Mourik:
Recursive Self-organizing Map as a Contractive Iterative Function System. IDEAL 2005: 327-334 - 2004
- [j2]Ping Li, Igor Farkas, Brian MacWhinney:
Early lexical development in a self-organizing neural network. Neural Networks 17(8-9): 1345-1362 (2004) - 2002
- [c1]Igor Farkas, Ping Li:
Modeling the Development of Lexicon with a Growing Self-Organizing Map. JCIS 2002: 553-556
1990 – 1999
- 1998
- [j1]Roman Rosipal, Milos Koska, Igor Farkas:
Prediction of Chaotic Time-Series with a Resource-Allocating RBF Network. Neural Process. Lett. 7(3): 185-197 (1998)
Coauthor Index
aka: Kristína Rebrová
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last updated on 2024-11-19 21:48 CET by the dblp team
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