Concept
Dirac delta function
Related publications (140)
Stable parameterization of continuous and piecewise-linear functions
Michaël Unser, Alexis Marie Frederic Goujon, Joaquim Gonçalves Garcia Barreto Campos
Rectified-linear-unit (ReLU) neural networks, which play a prominent role in deep learning, generate continuous and piecewise-linear (CPWL) functions. While they provide a powerful parametric representation, the mapping between the parameter and function s ...
2023Polynomial-time universality and limitations of deep learning
The goal of this paper is to characterize function distributions that general neural networks trained by descent algorithms (GD/SGD), can or cannot learn in polytime. The results are: (1) The paradigm of general neural networks trained by SGD is poly-time ...
WILEY2023Incentive Mechanism in the Sponsored Content Market With Network Effects
We propose an incentive mechanism for the sponsored content provider (CP) market in which the communication of users can be represented by a graph, and the private information of the users is assumed to have a continuous distribution function. The CP stipu ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023Thermal Evolution of Dirac Magnons in the Honeycomb Ferromagnet CrBr3
Bruce Normand, Christian Rüegg
CrBr3 is an excellent realization of the two-dimensional honeycomb ferromagnet, which offers a bosonic equivalent of graphene with Dirac magnons and topological character. We perform inelastic neutron scattering measurements using state-of-the-art instrume ...
2022Memoryless Worker-Task Assignment with Polylogarithmic Switching Cost
We study the basic problem of assigning memoryless workers to tasks with dynamically changing demands. Given a set of w workers and a multiset T ⊆ [t] of |T| = w tasks, a memoryless worker-task assignment function is any function ϕ that assigns the workers ...
Schloss Dagstuhl -- Leibniz-Zentrum fur Informatik2022A Benders decomposition for maximum simulated likelihood estimation of advanced discrete choice models
Michel Bierlaire, Claudia Bongiovanni
In this paper, we formulate a mixed integer linear program (MILP) for the simulated maximum likelihood estimation (MLSE) problem and devise a Benders decomposition approach to speed up the solution process. This framework can be applied to any advanced dis ...
2022A Benders decomposition for maximum simulated likelihood estimation of advanced discrete choice models
Michel Bierlaire, Claudia Bongiovanni
In this paper, we formulate a mixed integer linear program (MILP) for the simulated maximum likelihood estimation (MLSE) problem and devise a Benders decomposition approach to speed up the solution process. This framework can be applied to any advanced dis ...
2022A Benders decomposition for maximum simulated likelihood estimation of advanced discrete choice models
Michel Bierlaire, Claudia Bongiovanni
In this paper, we formulate a mixed integer linear program (MILP) for the simulated maximum likelihood estimation (MLSE) problem and devise a Benders decomposition approach to speed up the solution process. This framework can be applied to any advanced dis ...
2022