Standard Deep Generative Models for Density Estimation in Configuration Spaces: A Study of Benefits, Limits and Challenges

Robert Gieselmann, Florian T. Pokorny. Standard Deep Generative Models for Density Estimation in Configuration Spaces: A Study of Benefits, Limits and Challenges. In IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020, Las Vegas, NV, USA, October 24, 2020 - January 24, 2021. pages 5238-5245, IEEE, 2020. [doi]

@inproceedings{GieselmannP20,
  title = {Standard Deep Generative Models for Density Estimation in Configuration Spaces: A Study of Benefits, Limits and Challenges},
  author = {Robert Gieselmann and Florian T. Pokorny},
  year = {2020},
  doi = {10.1109/IROS45743.2020.9340994},
  url = {https://doi.org/10.1109/IROS45743.2020.9340994},
  researchr = {https://researchr.org/publication/GieselmannP20},
  cites = {0},
  citedby = {0},
  pages = {5238-5245},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020, Las Vegas, NV, USA, October 24, 2020 - January 24, 2021},
  publisher = {IEEE},
  isbn = {978-1-7281-6212-6},
}