| Limitation of capsule networks D Peer, S Stabinger, A Rodriguez-Sanchez Pattern Recognition Letters 144, 68 - 74, 2021 | 39* | 2021 |
| Increasing the adversarial robustness and explainability of capsule networks with -capsules D Peer, S Stabinger, A Rodriguez-Sanchez https://arxiv.org/abs/1812.09707, 2019 | 32* | 2019 |
| Greedy-layer Pruning: Speeding up Transformer Models for Natural Language Processing D Peer, S Stabinger, S Engl, A Rodriguez-Sanchez Pattern Recognition Letters, 2022, 2022 | 25* | 2022 |
| 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 |
| Training Deep Capsule Networks with Residual Connections J Gugglberger, D Peer, A Rodriguez-Sanchez International Conference on Artificial Neural Networks (ICANN) 30, 2021 | 13 | 2021 |
| 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 |
| Resilient techniques against disruptions of volatile cloud resources S Ristov, T Fahringer, D Peer, TP Pham, M Gusev, C Mas-Machuca Guide to Disaster-Resilient Communication Networks, 379-400, 2020 | 5 | 2020 |
| Momentum capsule networks J Gugglberger, D Peer, A Rodríguez-Sánchez arXiv preprint arXiv:2201.11091, 2022 | 3 | 2022 |
| Auto-tuning of deep neural networks by conflicting layer removal D Peer, S Stabinger, A Rodriguez-Sanchez arXiv preprint arXiv:2103.04331, 2021 | 3 | 2021 |
| DEEP LEARNING FOR REAL-TIME AVALANCHE DETECTION IN WEBCAM IMAGES J Fox, A Siebenbrunner, S Reitinger, D Peer, A Rodrıguez-Sánchez | 2 | 2023 |
| conflicting_bundle. py—A python module to identify problematic layers in deep neural networks D Peer, S Stabinger, A Rodríguez-Sánchez Software Impacts, 100053, 2021 | 2 | 2021 |
| Improving 3D Point Cloud Reconstruction with Dynamic Tree-Structured Capsules C Engelhardt, J Mittelberger, D Peer, S Stabinger, A Rodríguez-Sánchez 2022 IEEE 5th International Conference on Image Processing Applications and …, 2022 | 1 | 2022 |
| Improving the Trainability of Deep Neural Networks through Layerwise Batch-Entropy Regularization D Peer, B Keulen, S Stabinger, J Piater, A Rodríguez-Sánchez Transactions on Machine Learning Research (TMLR), 2022 | 1 | 2022 |
| ANLS*--A Universal Document Processing Metric for Generative Large Language Models D Peer, P Schöpf, V Nebendahl, A Rietzler, S Stabinger arXiv preprint arXiv:2402.03848, 2024 | | 2024 |
| Automating Avalanche Detection in Ground-Based Photographs with Deep Learning J Fox, A Siebenbrunner, S Reitinger, D Peer, A Rodrı́guez-Sánchez Available at SSRN 4537379, 2023 | | 2023 |
| Affordance detection with Dynamic-Tree Capsule Networks A Rodríguez-Sánchez, S Haller-Seeber, D Peer, C Engelhardt, ... 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids …, 2022 | | 2022 |
| Training of Feedforward Networks Fails on a Simple Parity-Task S Stabinger, D Peer, A Rodríguez-Sánchez, A Rodriguez NeurIPS 2020 pre-registration workshop, 2020 | | 2020 |
Antonio Rodriguez-SanchezAssociate Professor, University of InnsbruckVerified email at uibk.ac.at
