| Speeding up semantic segmentation for autonomous driving M Treml, J Arjona-Medina, T Unterthiner, R Durgesh, F Friedmann, ... MLITS, NIPS Workshop 2, 7, 2016 | 348 | 2016 |
| RUDDER: Return decomposition for delayed rewards JA Arjona-Medina, M Gillhofer, M Widrich, T Unterthiner, J Brandstetter, ... arXiv preprint arXiv:1806.07857, 2018 | 246 | 2018 |
| Explaining and interpreting LSTMs L Arras, J Arjona-Medina, M Widrich, G Montavon, M Gillhofer, KR Müller, ... Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 211-238, 2019 | 121 | 2019 |
| Visual scene understanding for autonomous driving using semantic segmentation M Hofmarcher, T Unterthiner, J Arjona-Medina, G Klambauer, ... Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 285-296, 2019 | 55 | 2019 |
| Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution VP Patil, M Hofmarcher, MC Dinu, M Dorfer, PM Blies, J Brandstetter, ... arXiv preprint arXiv:2009.14108, 2020 | 47 | 2020 |
| Convergence proof for actor-critic methods applied to ppo and rudder M Holzleitner, L Gruber, J Arjona-Medina, J Brandstetter, S Hochreiter Transactions on Large-Scale Data-and Knowledge-Centered Systems XLVIII …, 2021 | 37 | 2021 |
| XAI and Strategy Extraction via Reward Redistribution MC Dinu, M Hofmarcher, VP Patil, M Dorfer, PM Blies, J Brandstetter, ... International Workshop on Extending Explainable AI Beyond Deep Models and …, 2020 | 10 | 2020 |
| Computational Synteny Block: A framework to identify evolutionary events JA Arjona-Medina, O Trelles IEEE transactions on nanobioscience 15 (4), 343-353, 2016 | 9 | 2016 |
| Scene-adaptive radar tracking with deep reinforcement learning M Stephan, L Servadei, J Arjona-Medina, A Santra, R Wille, G Fischer Machine Learning with Applications 8, 100284, 2022 | 8 | 2022 |
| Computational workflow for the fine-grained analysis of metagenomic samples E Pérez-Wohlfeil, JA Arjona-Medina, O Torreno, E Ulzurrun, O Trelles BMC genomics 17, 351-361, 2016 | 8 | 2016 |
| RUDDER: Return decomposition for delayed rewards (2018) JA Arjona-Medina, M Gillhofer, M Widrich, T Unterthiner, J Brandstetter, ... ArXiv https://arxiv. org/abs, 1806 | 7 | 1806 |
| Deep reinforcement learning for optimization at early design stages L Servadei, JH Lee, JAA Medina, M Werner, S Hochreiter, W Ecker, ... IEEE Design & Test 40 (1), 43-51, 2022 | 5 | 2022 |
| Cost Optimization at Early Stages of Design Using Deep Reinforcement Learning L Servadei, J Zheng, J Arjona-Medina, M Werner, V Esen, S Hochreiter, ... Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD, 37-42, 2020 | 5 | 2020 |
| Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER. CoRR, abs/2012.01399 M Holzleitner, L Gruber, JA Arjona-Medina, J Brandstetter, S Hochreiter arXiv preprint arXiv:2012.01399, 2020 | 2 | 2020 |
| RUDDER: Return Decomposition for Delayed Rewards. arXiv e-prints, art JA Arjona-Medina, M Gillhofer, M Widrich, T Unterthiner, J Brandstetter, ... arXiv preprint arXiv:1806.07857, 2018 | 2 | 2018 |
| Refining borders of genome-rearrangements including repetitions JA Arjona-Medina, O Trelles BMC genomics 17, 433-445, 2016 | 2 | 2016 |
| MEET: A Monte Carlo Exploration-Exploitation Trade-Off for Buffer Sampling J Ott, L Servadei, J Arjona-Medina, E Rinaldi, G Mauro, DS Lopera, ... ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 1 | 2023 |
| Algorithms and methods for large-scale genome rearrangements identification JA Arjona Medina UMA Editorial, 2017 | 1 | 2017 |
| Analysis of Atom-level pretraining with QM data for Graph Neural Networks Molecular property models J Arjona-Medina, R Nugmanov arXiv preprint arXiv:2405.14837, 2024 | | 2024 |
| Algorithms and methods for large-scale genome rearrangements identification JAA Medina Universidad de Málaga, 2017 | | 2017 |
Sepp HochreiterInstitute for Machine Learning, Johannes Kepler University LinzEmail verificata su ml.jku.at
