Incorporating Interpretable Output Constraints in Bayesian Neural Networks

Empirische Inferenz

Author(s):	Yang, W. and Lorch, L. and Graule, M. and Lakkaraju, H. and Doshi-Velez, F.
Book Title:	Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
Volume:	33
Pages:	12721--12731
Year:	2020
Month:	December
Editors:	H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin
Publisher:	Curran Associates, Inc.

BibTeX Type:	Conference Paper (conference)

Event Name:	34th Annual Conference on Neural Information Processing Systems
Event Place:	Virtual Conference
State:	Published
URL:	https://proceedings.neurips.cc/paper/2020/file/95c7dfc5538e1ce71301cf92a9a96bd0-Paper.pdf

Electronic Archiving:	grant_archive

BibTeX

@conference{Yangetal20,
  title = {Incorporating Interpretable Output Constraints in Bayesian Neural Networks},
  booktitle = {Advances in Neural Information Processing Systems 33 (NeurIPS 2020)},
  volume = {33},
  pages = {12721--12731},
  editors = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
  publisher = {Curran Associates, Inc.},
  month = dec,
  year = {2020},
  author = {Yang, W. and Lorch, L. and Graule, M. and Lakkaraju, H. and Doshi-Velez, F.},
  url = {https://proceedings.neurips.cc/paper/2020/file/95c7dfc5538e1ce71301cf92a9a96bd0-Paper.pdf},
  month_numeric = {12}
}