Competence region estimation for black-box surrogate models - Tapan Shah (Proceedings of the International Florida Artificial Intelligence Research Society Conference)
With advances in edge applications for industry andhealthcare, machine learning models are increasinglytrained on the edge. However, storage and memory in-frastructure at the edge are often primitive, due to costand real-estate constraints. A simple, effective methodis to learn machine learning models from quantized datastored with low arithmetic precision (1-8 bits). In thiswork, we introduce two stochastic quantization meth-ods, dithering and stochastic rounding. In dithering, ad-ditive noise from a uniform distribution is added tothe sample before quantization. In stochastic rounding,each sample is quantized to the upper level with prob-ability p and to a lower level with probability 1-p. Thekey contributions of the paper are