Title: How Could Polyhedral Theory Harness Deep Learning?,
Abstract: The holy grail of deep learning is to come up with an automatic method to
design optimal architectures for different applications. In other words, how
can we effectively dimension and organize neurons along the network layers
based on the computational resources, input size, and amount of training data?
We outline promising research directions based on polyhedral theory and
mixed-integer representability that may offer an analytical approach to this
question, in contrast to the empirical techniques often employed.