| Adapt or get left behind: Domain adaptation through bert language model finetuning for aspect-target sentiment classification A Rietzler, S Stabinger, P Opitz, S Engl Proceedings of The 12th Language Resources and Evaluation Conference, 2019 | 249 | 2019 |
| 25 years of cnns: Can we compare to human abstraction capabilities? S Stabinger, A Rodríguez-Sánchez, J Piater Artificial Neural Networks and Machine Learning–ICANN 2016: 25th …, 2016 | 71 | 2016 |
| The effects of social gaze in human-robot collaborative assembly K Fischer, LC Jensen, F Kirstein, S Stabinger, Ö Erkent, D Shukla, J Piater Social Robotics: 7th International Conference, ICSR 2015, Paris, France …, 2015 | 41 | 2015 |
| Training deep capsule networks D Peer, S Stabinger, A Rodriguez-Sanchez arXiv preprint arXiv:1812.09707, 1-7, 2018 | 32* | 2018 |
| Limitation of capsule networks D Peer, S Stabinger, A Rodriguez-Sanchez Pattern Recognition Letters 144, 68-74, 2021 | 28 | 2021 |
| Greedy-layer pruning: Speeding up transformer models for natural language processing D Peer, S Stabinger, S Engl, A Rodríguez-Sánchez Pattern Recognition Letters 157, 76-82, 2022 | 21 | 2022 |
| Classification of tennis shots with a neural network approach A Ganser, B Hollaus, S Stabinger Sensors 21 (17), 5703, 2021 | 18 | 2021 |
| A deep learning approach for detecting and correcting highlights in endoscopic images A Rodríguez-Sánchez, D Chea, G Azzopardi, S Stabinger 2017 seventh international conference on image processing theory, tools and …, 2017 | 17 | 2017 |
| Evaluating the progress of deep learning for visual relational concepts S Stabinger, D Peer, J Piater, A Rodríguez-Sánchez Journal of Vision 21 (11), 8-8, 2021 | 15 | 2021 |
| Using wearable sensors and a convolutional neural network for catch detection in American football B Hollaus, S Stabinger, A Mehrle, C Raschner Sensors 20 (23), 6722, 2020 | 14 | 2020 |
| Evaluation of Deep Learning on an Abstract Image Classification Dataset S Stabinger, A Rodriguez-Sanchez Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 14 | 2017 |
| Limitations of routing-by-agreement based capsule networks D Peer, S Stabinger, A Rodriguez-Sanchez arXiv preprint arXiv:1905.08744, 2019 | 11 | 2019 |
| Arguments for the unsuitability of convolutional neural networks for non-local tasks S Stabinger, D Peer, A Rodríguez-Sánchez Neural Networks 142, 171-179, 2021 | 8 | 2021 |
| Conflicting bundles: Adapting architectures towards the improved training of deep neural networks D Peer, S Stabinger, A Rodriguez-Sanchez Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2021 | 8 | 2021 |
| Adversarial examples against a bert absa model–fooling bert with l33t, misspellign, and punctuation N Hofer, P Schöttle, A Rietzler, S Stabinger Proceedings of the 16th International Conference on Availability …, 2021 | 7 | 2021 |
| Evaluating CNNs on the gestalt principle of closure G Ehrensperger, S Stabinger, AR Sánchez International Conference on Artificial Neural Networks, 296-301, 2019 | 7 | 2019 |
| Autonomous skill-centric testing using deep learning S Hangl, S Stabinger, J Piater 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2017 | 5 | 2017 |
| International Conference on Artificial Neural Networks S Stabinger, A Rodríguez-Sánchez, J Piater | 5 | 2016 |
| Greedy layer pruning: Decreasing inference time of transformer models D Peer, S Stabinger, S Engl, A Rodrı́guez-Sánchez Available at SSRN 3985429, 2021 | 4 | 2021 |
| Learning abstract classes using deep learning S Stabinger, A Rodríguez-Sánchez, J Piater arXiv preprint arXiv:1606.05506, 2016 | 4 | 2016 |
Antonio Rodriguez-SanchezAssociate Professor, University of InnsbruckEmail verificata su uibk.ac.at
