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...

Abstract A new method to enclose the pseudospectrum via numerical range of inverse a matrix or linear operator is presented. The applied finite-dimensional discretizations an on infinite-dimensional Hilbert space, and convergence results for different approximation schemes are obtained, including finite element methods. We show that full contained in intersection sets which expressed terms rang...

‎Linear semi-infinite programming problem is an important class of optimization problems which deals with infinite constraints‎. ‎In this paper‎, ‎to solve this problem‎, ‎we combine a discretization method and a neural network method‎. ‎By a simple discretization of the infinite constraints,we convert the linear semi-infinite programming problem into linear programming problem‎. ‎Then‎, ‎we use...

We present a new discretization method based on Area under ROC Curve (AUC) measure. Maximum Area under ROC Curve Based Discretization (MAD) is a global, static and supervised discretization method. It discretizes a continuous feature in a way that the AUC based only on that feature is to be maximized. The proposed method is compared with alternative discretization methods such as Entropy-MDLP (...