The search cost of neural architecture (NAS) has been largely reduced by differentiable and weight-sharing methods. Such methods optimize a super-network with all possible edges operations, determine the optimal sub-network discretization, i.e., pruning off operations/edges small weights. However, discretization process performed on either operations or incurs significant inaccuracy thus qualit...

For fractional-order differentiator where is a real number, its discretization is a key step in digital implementation. Two discretization methods are presented. The first scheme is a direct recursive discretization of the Tustin operator. The second one is a direct discretization method using the Al-Alaoui operator via continued fraction expansion (CFE). The approximate discretization is minim...