| Towards multimodal neural robot learning S Wermter, C Weber, M Elshaw, C Panchev, H Erwin, F Pulvermüller Robotics and Autonomous Systems 47 (2-3), 171-175, 2004 | 58 | 2004 |
| Biomimetic neural learning for intelligent robots: Intelligent systems, cognitive robotics, and neuroscience S Wermter, G Palm, M Elshaw Springer, 2005 | 47 | 2005 |
| Stacked deep convolutional auto-encoders for emotion recognition from facial expressions A Ruiz-Garcia, M Elshaw, A Altahhan, V Palade 2017 International Joint Conference on Neural Networks (IJCNN), 1586-1593, 2017 | 35 | 2017 |
| A hybrid generative and predictive model of the motor cortex C Weber, S Wermter, M Elshaw Neural Networks 19 (4), 339-353, 2006 | 29 | 2006 |
| The application of genetic algorithms to sensor parameter selection for multisensor array configuration P Corcoran, J Anglesea, M Elshaw Sensors and Actuators A: Physical 76 (1-3), 57-66, 1999 | 27 | 1999 |
| Deep learning for emotion recognition in faces A Ruiz-Garcia, M Elshaw, A Altahhan, V Palade International Conference on Artificial Neural Networks, 38-46, 2016 | 26 | 2016 |
| Learning robot actions based on self-organising language memory S Wermter, M Elshaw Neural Networks 16 (5-6), 691-699, 2003 | 25 | 2003 |
| Emotional recognition from the speech signal for a virtual education agent A Tickle, S Raghu, M Elshaw Journal of Physics: Conference Series 450 (1), 012053, 2013 | 22 | 2013 |
| An associator network approach to robot learning by imitation through vision, motor control and language M Elshaw, C Weber, A Zochios, S Wermter 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No …, 2004 | 20 | 2004 |
| Towards novel neuroscience-inspired computing S Wermter, M Elshaw, J Austin, D Willshaw Emergent neural computational architectures based on neuroscience, 1-19, 2001 | 20 | 2001 |
| Grounding neural robot language in action S Wermter, C Weber, M Elshaw, V Gallese, F Pulvermüller Biomimetic neural learning for intelligent robots, 162-181, 2005 | 17* | 2005 |
| A hybrid deep learning neural approach for emotion recognition from facial expressions for socially assistive robots A Ruiz-Garcia, M Elshaw, A Altahhan, V Palade Neural Computing and Applications 29 (7), 359-373, 2018 | 16 | 2018 |
| Reinforcement Learning C Weber, M Elshaw, NM Mayer BoD–Books on Demand, 2008 | 15 | 2008 |
| Pedestrian and cyclist detection and intent estimation for autonomous vehicles: a survey S Ahmed, MN Huda, S Rajbhandari, C Saha, M Elshaw, S Kanarachos Applied Sciences 9 (11), 2335, 2019 | 13 | 2019 |
| Towards integrating learning by demonstration and learning by instruction in a multimodal robot S Wermter, M Elshaw, C Weber, C Panchev, H Erwin Proceedings of the IROS-2003 Workshop on Robot Learning by Demonstration, 72-79, 2003 | 13 | 2003 |
| A modular approach to self-organization of robot control based on language instruction S Wermter, M Elshaw, S Farrand Connection Science 15 (2-3), 73-94, 2003 | 13 | 2003 |
| Reinforcement learning to support meta-level control in air traffic management DP Alves, L Weigang, BB Souza, C Weber, M Elshaw, N Mayer Reinforcement Learning–Theory and Applications, 409-424, 2008 | 12 | 2008 |
| Self-Organising Networks for Classification Learning from Normal and Aphasic Speech S Garfield, M Elshaw, S Wermter Proceedings of the Annual Meeting of the Cognitive Science Society 23 (23), 2001 | 11 | 2001 |
| Reinforcement learning: theory and applications C Weber, M Elshaw, NM Mayer I-TECH Education and Pub., 2008 | 10 | 2008 |
| Deep learning for illumination invariant facial expression recognition A Ruiz-Garcia, V Palade, M Elshaw, I Almakky 2018 International Joint Conference on Neural Networks (IJCNN), 1-6, 2018 | 9 | 2018 |
