| A simulator for self-adaptive energy demand management Y Guo, R Li, G Poulton, A Zeman 2008 Second IEEE International Conference on Self-Adaptive and Self …, 2008 | 40 | 2008 |
| Clustering distributed energy resources for large-scale demand management E Ogston, A Zeman, M Prokopenko, G James First International Conference on Self-Adaptive and Self-Organizing Systems …, 2007 | 31 | 2007 |
| Performance of multi-agent coordination of distributed energy resources J Li, G Poulton, G James, A Zeman, P Wang, M Chadwick, M Piraveenan WSEAS Transactions on System and Control 2 (1), 52-58, 2007 | 25 | 2007 |
| Orthogonal Representations of Object Shape and Category in Deep Convolutional Neural Networks and Human Visual Cortex AA Zeman, JB Ritchie, S Bracci, HO de Beeck Scientific Reports 10 (1), 1-12, 2020 | 24 | 2020 |
| A reinforcement learning approach to setting multi-objective goals for energy demand management Y Guo, A Zeman, R Li International Journal of Agent Technologies and Systems (IJATS) 1 (2), 55-70, 2009 | 21 | 2009 |
| The Müller-Lyer illusion in a computational model of biological object recognition A Zeman, O Obst, KR Brooks, AN Rich Plos One 8 (2), e56126, 2013 | 19 | 2013 |
| Robot Assistants for Perimetry: A Study of Patient Experience and Performance AM McKendrick, A Zeman, P Liu, D Aktepe, I Aden, D Bhagat, K Do, ... Translational vision science & technology 8 (3), 59-59, 2019 | 17 | 2019 |
| Utility simulation tool for automated energy demand side management Y Guo, A Zeman, R Li Agent technology for energy systems ATES 6, 8, 2010 | 15 | 2010 |
| Adaptive control of distributed energy management: A comparative study A Zeman, M Prokopenko, Y Guo, R Li 2008 Second IEEE International Conference on Self-Adaptive and Self …, 2008 | 14 | 2008 |
| An exponential filter model predicts lightness illusions A Zeman, KR Brooks, S Ghebreab Frontiers in human neuroscience 9, 368, 2015 | 9 | 2015 |
| Complex cells decrease errors for the Müller-Lyer illusion in a model of the visual ventral stream A Zeman, O Obst, KR Brooks Frontiers in computational neuroscience 8, 112, 2014 | 9 | 2014 |
| Decentralized multi-agent clustering in scale-free sensor networks M Piraveenan, M Prokopenko, P Wang, A Zeman Computational Intelligence: A Compendium, 485-515, 2008 | 8 | 2008 |
| Untangling the animacy organization of occipitotemporal cortex JB Ritchie, AA Zeman, J Bosmans, S Sun, K Verhaegen, HPO de Beeck Journal of Neuroscience 41 (33), 7103-7119, 2021 | 7 | 2021 |
| Homeotaxis: Coordination with persistent time-loops M Prokopenko, A Zeman, R Li International Conference on Simulation of Adaptive Behavior, 403-414, 2008 | 7 | 2008 |
| Symbiotic sensor networks in complex underwater terrains: A simulation framework V Gerasimov, G Healy, M Prokopenko, P Wang, A Zeman International Conference on Knowledge-Based and Intelligent Information and …, 2006 | 4 | 2006 |
| Training for object recognition with increasing spatial frequency: A comparison of deep learning with human vision LK Avberšek, A Zeman, HO de Beeck Journal of Vision 21 (10), 14-14, 2021 | 3 | 2021 |
| Object-scene conceptual regularities reveal fundamental differences between biological and artificial object vision S Bracci, J Mraz, A Zeman, G Leys, HO de Beeck bioRxiv, 2021 | 3 | 2021 |
| Robot world cup soccer 2004: The magic of uts unleashed M Chang, G Dissanayake, D Gurram, F Hadzic, G Healy, A Karol, ... | 3 | 2004 |
| Deep Learning for Human Embryo Classification at the Cleavage Stage (Day 3) A Zeman, AS Maerten, A Mengels, LF Sharon, C Spiessens, HO Beeck International Conference on Pattern Recognition, 278-292, 2021 | 1 | 2021 |
| Computational modelling of visual illusions A Zeman, B Eng, DE Prac Macquarie University, Faculty of Human Sciences, Department of Cognitive Science, 2015 | 1 | 2015 |
Mikhail ProkopenkoProfessor, Centre for Complex Systems, University of SydneyVerified email at sydney.edu.au
