Robustness verification of deep neural networks using star-based reachability analysis with variable-length time series input (bibtex)

by Neelanjana Pal, Diego Manzanas Lopez and Taylor T. Johnson

Reference:

Neelanjana Pal, Diego Manzanas Lopez and Taylor T. Johnson, "Robustness verification of deep neural networks using star-based reachability analysis with variable-length time series input", In ERCIM Working Group 28th International Conference on Formal Methods for Industrial Critical Systems (FMICS'23), Springer, pp. 170–188, 2023.

Bibtex Entry:

@inproceedings{pal2023fmics,
  title = {Robustness verification of deep neural networks using star-based reachability analysis with variable-length time series input},
  author = {Neelanjana Pal and Diego Manzanas Lopez and Taylor T. Johnson},
  year = {2023},
  month = sep,
  booktitle = {ERCIM Working Group 28th International Conference on Formal Methods for Industrial Critical Systems (FMICS'23)},
  pages = {170--188},
  publisher = {Springer},
  doi = {10.1007/978-3-031-43681-9_10},
  keywords = {predictive maintenance,neural network verification,time-series},
  publabel = {C41},
  pubtype = {C},
  dblp = {conf/fmics/PalLJ23},
  pdf = "research/pal2023fmics.pdf",
}