Hue Dang, Matthew Wicker, Goetz Botterweck, and Andrea Patane address the problem of computing robustness guarantees for neural networks against the quantisation of inputs, parameters, and activation values by bounding the worst-case discrepancy between an original neural network and all possible quantised versions parametrised by a maximum quantisation diameter epsilon greater than zero.

Authors

• Person: Samuel Tesfazgi, Leonhard Sprandl, Sandra Hirche Organization: AISTATS
  Track: Poster Session 3 - aistats 2026

Sources

• Track: Poster Session 3 - aistats 2026 virtual.aistats.org Samuel Tesfazgi, Leonhard Sprandl, Sandra Hirche · AISTATS via serper

Referenced by nodes (1)

• artificial neural networks concept