Corpus ID: 155091375

Output-Constrained Bayesian Neural Networks

@article{Yang2019OutputConstrainedBN,
  title={Output-Constrained Bayesian Neural Networks},
  author={W. Yang and Lars Lorch and M. Graule and S. Srinivasan and A. Suresh and Jiayu Yao and Melanie F. Pradier and Finale Doshi-Velez},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.06287}
}
  • W. Yang, Lars Lorch, +5 authors Finale Doshi-Velez
  • Published 2019
  • Mathematics, Computer Science
  • ArXiv
  • Bayesian neural network (BNN) priors are defined in parameter space, making it hard to encode prior knowledge expressed in function space. We formulate a prior that incorporates functional constraints about what the output can or cannot be in regions of the input space. Output-Constrained BNNs (OC-BNN) represent an interpretable approach of enforcing a range of constraints, fully consistent with the Bayesian framework and amenable to black-box inference. We demonstrate how OC-BNNs improve model… CONTINUE READING
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