| Continual lifelong learning with neural networks: A review GI Parisi, R Kemker, JL Part, C Kanan, S Wermter Neural networks 113, 54-71, 2019 | 2546 | 2019 |
| An overview of hybrid neural systems S Wermter, R Sun International Workshop on Hybrid Neural Systems, 1-13, 1998 | 299 | 1998 |
| An analysis of convolutional long short-term memory recurrent neural networks for gesture recognition E Tsironi, P Barros, C Weber, S Wermter Neurocomputing 268, 76-86, 2017 | 235 | 2017 |
| Hybrid neural systems: from simple coupling to fully integrated neural networks K McGarry, S Wermter, J MacIntyre Neural Computing Surveys 2 (1), 62-93, 1999 | 167 | 1999 |
| Semantic object accuracy for generative text-to-image synthesis T Hinz, S Heinrich, S Wermter IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (3), 1552-1565, 2022 | 154 | 2022 |
| Face expression recognition with a 2-channel convolutional neural network D Hamester, P Barros, S Wermter 2015 International Joint Conference on Neural Networks (IJCNN), 1787-1794, 2015 | 152 | 2015 |
| Lifelong learning of spatiotemporal representations with dual-memory recurrent self-organization GI Parisi, J Tani, C Weber, S Wermter Frontiers in neurorobotics 12 (Article 78), 2018 | 146 | 2018 |
| Efficient facial feature learning with wide ensemble-based convolutional neural networks H Siqueira, S Magg, S Wermter Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5800-5809, 2020 | 138 | 2020 |
| Speeding up the hyperparameter optimization of deep convolutional neural networks T Hinz, N Navarro-Guerrero, S Magg, S Wermter International Journal of Computational Intelligence and Applications 17 (02 …, 2018 | 133 | 2018 |
| Improved techniques for training single-image gans T Hinz, M Fisher, O Wang, S Wermter Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2021 | 129 | 2021 |
| Socially assistive robots: a comprehensive approach to extending independent living DO Johnson, RH Cuijpers, JF Juola, E Torta, M Simonov, A Frisiello, ... International Journal of Social Robotics 6 (2), 195-211, 2014 | 128 | 2014 |
| Lifelong learning of human actions with deep neural network self-organization GI Parisi, J Tani, C Weber, S Wermter Neural Networks 96, 137-149, 2017 | 123 | 2017 |
| Generating multiple objects at spatially distinct locations T Hinz, S Heinrich, S Wermter arXiv preprint arXiv:1901.00686, 2019 | 115 | 2019 |
| Hybrid connectionist natural language processing S Wermter Chapman & Hall, 1995 | 113 | 1995 |
| The OMG-emotion behavior dataset P Barros, N Churamani, E Lakomkin, H Siqueira, A Sutherland, S Wermter 2018 International Joint Conference on Neural Networks (IJCNN), 1-7, 2018 | 107 | 2018 |
| Developing crossmodal expression recognition based on a deep neural model P Barros, S Wermter Adaptive behavior 24 (5), 373-396, 2016 | 104 | 2016 |
| Connectionist, statistical and symbolic approaches to learning for natural language processing S Wermter, E Riloff, G Scheler Springer, 1996 | 102 | 1996 |
| Self-organizing neural integration of pose-motion features for human action recognition GI Parisi, C Weber, S Wermter Frontiers in Neurorobotics 9, 3, 2015 | 100 | 2015 |
| Multimodal emotional state recognition using sequence-dependent deep hierarchical features P Barros, D Jirak, C Weber, S Wermter Neural Networks 72, 140-151, 2015 | 99 | 2015 |
| Data mining using rule extraction from Kohonen self-organising maps J Malone, K McGarry, S Wermter, C Bowerman Neural Computing & Applications 15, 9-17, 2006 | 98 | 2006 |
Cornelius WeberUniversität Hamburg在 informatik.uni-hamburg.de 的电子邮件经过验证
