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MPI Year Book 2017

Computing with Uncertainty

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Probabilistic Numerics, Empirical Inference
Philipp Hennig
Affiliated Researcher

Machine learning requires computer hardware to reliable and efficiently compute estimations for ever more complex and fundamentally incomputable quantities. A research team at MPI for Intelligent Systems in Tübingen develops new algorithms which purposely lower the precision of computations and return an explicit measure of uncertainty over the correct result alongside the estimate. Doing so allows for more flexible management of resources, and increases the reliability of intelligent systems.

Author(s): Hennig, Philipp
Year: 2017
Bibtex Type: MPI Year Book (mpi_year_book)
DOI: 10.17617/1.54
Electronic Archiving: grant_archive
URL: https://www.mpg.de/10994958/mpi-mf_jb_2017?c=11741001

BibTex 

@mpi_year_book{year_book_hennig_2017,
  title = {Computing with Uncertainty},
  abstract = {Machine learning requires computer hardware to reliable and efficiently compute estimations for ever more complex and fundamentally incomputable quantities. A research team at MPI for Intelligent Systems in Tübingen develops new algorithms which purposely lower the precision of computations and return an explicit measure of uncertainty over the correct result alongside the estimate. Doing so allows for more flexible management of resources, and increases the reliability of intelligent systems.},
  year = {2017},
  slug = {year_book_hennig_2017},
  author = {Hennig, Philipp},
  url = {https://www.mpg.de/10994958/mpi-mf_jb_2017?c=11741001}
}