World Simulation
Deep Generative Models
Publications
2023
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)
2022
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)
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)
2021
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)
2020
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)
2019
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)
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)