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 | 114 | 2019 |
Towards novel neuroscience-inspired computing S Wermter, M Elshaw, J Austin, D Willshaw Emergent Neural Computational Architectures Based on Neuroscience: Towards …, 2001 | 92 | 2001 |
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, 359-373, 2018 | 79 | 2018 |
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 | 68 | 2017 |
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 | 59 | 2004 |
Biomimetic neural learning for intelligent robots: Intelligent systems, cognitive robotics, and neuroscience S Wermter, G Palm, M Elshaw Springer, 2005 | 52 | 2005 |
Deep learning for emotion recognition in faces A Ruiz-Garcia, M Elshaw, A Altahhan, V Palade Artificial Neural Networks and Machine Learning–ICANN 2016: 25th …, 2016 | 45 | 2016 |
A hybrid generative and predictive model of the motor cortex C Weber, S Wermter, M Elshaw Neural Networks 19 (4), 339-353, 2006 | 32 | 2006 |
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 | 30 | 2013 |
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 | 28 | 1999 |
Learning robot actions based on self-organising language memory S Wermter, M Elshaw Neural Networks 16 (5-6), 691-699, 2003 | 27 | 2003 |
Emotion recognition from face images in an unconstrained environment for usage on social robots N Webb, A Ruiz-Garcia, M Elshaw, V Palade 2020 international joint conference on neural networks (IJCNN), 1-8, 2020 | 24 | 2020 |
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 | 23 | 2004 |
Deep learning for real time facial expression recognition in social robots A Ruiz-Garcia, N Webb, V Palade, M Eastwood, M Elshaw Neural Information Processing: 25th International Conference, ICONIP 2018 …, 2018 | 21 | 2018 |
Reinforcement Learning C Weber, M Elshaw, NM Mayer BoD–Books on Demand, 2008 | 20 | 2008 |
Grounding neural robot language in action S Wermter, C Weber, M Elshaw, V Gallese, F Pulvermüller Biomimetic Neural Learning for Intelligent Robots: Intelligent Systems …, 2005 | 19* | 2005 |
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 | 14 | 2018 |
Reinforcement learning: Theory and applications C Weber, M Elshaw, NM Mayer I-TECH Education and Pub., 2008 | 13 | 2008 |
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 | 12 | 2003 |