Computational Synteny Block: A framework to identify evolutionary events. - Jose A. Arjona-Medina; Oswaldo Trelles (BIBM, 2015)

RUDDER: Return Decomposition for Delayed Rewards. - Jose A. Arjona-Medina; Michael Gillhofer; Michael Widrich; Thomas Unterthiner; Sepp Hochreiter (CoRR, 2018)

RUDDER: Return Decomposition for Delayed Rewards. - Jose A. Arjona-Medina; Michael Gillhofer; Michael Widrich; Thomas Unterthiner; Johannes Brandstetter; Sepp Hochreiter (NeurIPS, 2019)

Explaining and Interpreting LSTMs. - Leila Arras; Jose A. Arjona-Medina; Michael Widrich; Grégoire Montavon; Michael Gillhofer; Klaus-Robert Müller; Sepp Hochreiter; Wojciech Samek (CoRR, 2019)

Explaining and Interpreting LSTMs. - Leila Arras; Jose A. Arjona-Medina; Michael Widrich; Grégoire Montavon; Michael Gillhofer; Klaus-Robert Müller; Sepp Hochreiter; Wojciech Samek (Explainable AI, 2019)

Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation. - Markus Hofmarcher; Thomas Unterthiner; Jose A. Arjona-Medina; Günter Klambauer; Sepp Hochreiter; Bernhard Nessler (Explainable AI, 2019)

XAI and Strategy Extraction via Reward Redistribution. - Marius-Constantin Dinu; Markus Hofmarcher; Vihang Prakash Patil; Matthias Dorfer; Patrick M. Blies; Johannes Brandstetter; Jose A. Arjona-Medina; Sepp Hochreiter (xxAI@ICML, 2020)

Cost Optimization at Early Stages of Design Using Deep Reinforcement Learning. - Lorenzo Servadei; Jiapeng Zheng; Jose A. Arjona-Medina; Michael Werner; Volkan Esen; Sepp Hochreiter; Wolfgang Ecker; Robert Wille (MLCAD, 2020)

Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER. - Markus Holzleitner; Lukas Gruber; José Antonio Arjona-Medina; Johannes Brandstetter; Sepp Hochreiter (Trans. Large Scale Data Knowl. Centered Syst., 2021)

Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution. - Vihang Patil; Markus Hofmarcher; Marius-Constantin Dinu; Matthias Dorfer; Patrick M. Blies; Johannes Brandstetter; José Antonio Arjona-Medina; Sepp Hochreiter (ICML, 2022)

MEET: A Monte Carlo Exploration-Exploitation Trade-Off for Buffer Sampling. - Julius Ott; Lorenzo Servadei; Jose A. Arjona-Medina; Enrico Rinaldi; Gianfranco Mauro; Daniela Sanchez Lopera; Michael Stephan; Thomas Stadelmayer; Avik Santra; Robert Wille (ICASSP, 2023)

Deep Reinforcement Learning for Optimization at Early Design Stages. - Lorenzo Servadei; Jin Hwa Lee; José Antonio Arjona-Medina; Michael Werner; Sepp Hochreiter; Wolfgang Ecker; Robert Wille (IEEE Des. Test, 2023)

Atom-Level Quantum Pretraining Enhances the Spectral Perception of Molecular Graphs in Graphormer. - Alessio Fallani; José Antonio Arjona-Medina; Konstantin Chernichenko; Ramil I. Nugmanov; Jörg Kurt Wegner; Alexandre Tkatchenko (AIDD@ICANN, 2024)

Analysis of Atom-level pretraining with Quantum Mechanics (QM) data for Graph Neural Networks Molecular property models. - Jose A. Arjona-Medina; Ramil I. Nugmanov (CoRR, 2024)

Pretraining graph transformers with atom-in-a-molecule quantum properties for improved ADMET modeling. - Alessio Fallani; Ramil I. Nugmanov; Jose A. Arjona-Medina; Jörg Kurt Wegner; Alexandre Tkatchenko; Kostiantyn Chernichenko (J. Cheminformatics, 2025)

Benchmarking molecular conformer augmentation with context-enriched training: graph-based transformer versus GNN models. - Cecile Valsecchi; Jose A. Arjona-Medina; Natalia Dyubankova; Ramil I. Nugmanov (J. Cheminformatics, 2025)