World Simulation

T. Schmied, M. Hofmarcher, F. Paischer, R. Pascanu, and S. Hochreiter (2023) Learning to Modulate Pre-trained Models in RL. arXiv:2306.14884, 2023-06-26. (more) (download)

F. Paischer, T. Adler, M. Hofmarcher, and S. Hochreiter (2023) Semantic HELM: An Interpretable Memory for Reinforcement Learning. arXiv:2306.09312, 2023-06-15. (more) (download)

K. Tertikas, P. Despoina, B. Pan, J. J. Park, M. A. Uy, I. Emiris, Y. Avrithis, and L. Guibas (2023) PartNeRF: Generating Part-Aware Editable 3D Shapes without 3D Supervision. arXiv:2303.09554, 2023-03-16. (more) (download)

Y. Xu, F. He, B. Du, D. Tao, and L. Zhang (2022) Self-Ensembling GAN for Cross-Domain Semantic Segmentation. IEEE Transactions on Multimedia, 2022-12-29. (more) (download)

R. Siripurapu, V. P. Patil, K. Schweighofer, M.-C. Dinu, T. Schmied, L. E. F. Diez, M. Holzleitner, H. Eghbal-Zadeh, M. K. Kopp, and S. Hochreiter (2022) InfODist: Online Distillation with Informative Rewards Improves Generalization in Curriculum Learning. Deep Reinforcement Learning Workshop at NeurIPS 2022, 2022-12-09. (more) (download)

K. Schweighofer, M.-c. Dinu, A. Radler, M. Hofmarcher, V. P. Patil, A. Bitto-Nemling, H. Eghbal-zadeh, and S. Hochreiter (2022) A Dataset Perspective on Offline Reinforcement Learning. Conference on Lifelong Learning Agents, Proceedings of Machine Learning Research, 199, 470-517, 2022-11-28. (more) (download)

C. Steinparz, T. Schmied, F. Paischer, M.-C. Dinu, V. Patil, A. Bitto-Nemling, H. Eghbal-zadeh, and S. Hochreiter (2022) Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning. Proceedings of The 1st Conference on Lifelong Learning Agents, PMLR, 199, 441-469, 2022, 2022-11-28. (more) (download)

Z. He, K. Xia, P. Ghamisi, Y. Hu, S. Fan, and B. Zu (2022) HyperViTGAN: Semisupervised Generative Adversarial Network With Transformer for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 6053-6068, 2022-07-18. (more) (download)

F. Paischer, T. Adler, V. Patil, A. Bitto-Nemling, M. Holzleitner, S. Lehner, H. Eghbal-zadeh, and S. Hochreiter (2022) History Compression via Language Models in Reinforcement Learning. Proceedings of the 39th International Conference on Machine Learning, PMLR, 162, 17156-17185, 2022-06-28. (more) (download)

L. Servadei, J. H. Lee, J. A. A. Medina, M. Werner, S. Hochreiter, W. Ecker, and R. Wille (2022) Deep Reinforcement Learning for Optimization at Early Design Stages. IEEE Design & Test, 2022-01-20. (more) (download)

K. Schweighofer, M. Hofmarcher, M.-C. Dinu, P. Renz, A. Bitto-Nemling, V. Patil, and S. Hochreiter (2021) Understanding the Effects of Dataset Characteristics on Offline Reinforcement Learning. arXiv:2111.04714, 2021-11-08. (more) (download)

D. Flam-Shepherd, T. Wu, X. Gu, A. Cervera-Lierta, M. Krenn, and A. Aspuru-Guzik (2021) Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models. arXiv:2109.02490, 2021-09-06. (more) (download)

C. Shen, M. Krenn, S. Eppel, and A. Aspuru-Guzik (2021) Deep Molecular Dreaming: Inverse Machine Learning for De-novo Molecular Design and Interpretability with Surjective Representations. Machine Learning: Science and Technology, 2, 3, 03LT02, 2021-07-13. (more) (download)

M. Holzleitner, L. Gruber, J. Arjona-Medina, J. Brandstetter, and S. Hochreiter (2020) Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER. arXiv:2012.01399, 2020-12-02. (more) (download)

L. Servadei, J. Zheng, J. Arjona-Medina, M. Werner, V. Esen, S. Hochreiter, W. Ecker, and R. Wille (2020) Cost Optimization at Early Stages of Design Using Deep Reinforcement Learning. Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD, 37-42, 2020-11-16. (more) (download)

P. Renz, D. Van Rompaey, J. K. Wegner, S. Hochreiter, and G. Klambauer (2020) On failure modes in molecule generation and optimization. Drug Discovery Today: Technologies, 32, 55-63, 2020-10-24. (more) (download)

V. P. Patil, M. Hofmarcher, M.-C. Dinu, M. Dorfer, P. M. Blies, J. Brandstetter, J. A. Arjona-Medina, and S. Hochreiter (2020) Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution. arXiv:2009.14108, 2020-09-29. (more) (download)

M. Gillhofer, H. Ramsauer, J. Brandstetter, and S. Hochreiter (2019) A GAN based solver of black-box inverse problems. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019); e-print at openreview.net. (more) (download)

J. A. Arjona-Medina, M. Gillhofer, M. Widrich, T. Unterthiner, J. Brandstetter, and S. Hochreiter (2019) RUDDER – Return Decomposition with Delayed Rewards. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 13566;  e-print also at arXiv:1806.07857v3, 2019-09-10. (more) (download)

D. Jonietz & M. Kopp (2019) Towards Modeling Geographical Processes with Generative Adversarial Networks (GANs). 14th International Conference on Spatial Information Theory (COSIT 2019), Leibniz International Proceedings in Informatics (LIPIcs), 142, 27, 2019-09-03. (more) (download)