Simon Fraser University

We consider the problem of answering queries about formulas of first-order logic based on background knowledge partially represented explicitly as other formulas, and partially represented as examples independently drawn from a fixed probability...

This talk will focus on two topics in the foundations of neural networks: computability and false structures. In the first part I will recall briefly the concept of recursive functions and then discuss the gap between existence results, the...

Neural networks are remarkable families of non-linear functions that form the backbone for state-of-the-art algorithms in tasks from computer vision (self-driving cars), natural language processing (Google Translate) and reinforcement learning...

A challenge in descriptive complexity is to identify logics with low complexity that simultaneously express both fundamental reachability properties and counting properties on unordered structures. In this talk, we introduce a family of logics that...

The aim of structured optimization is to assemble a solution, using a given set of atoms, to fit a model to data. Polarity, which generalizes the notion of orthogonality from linear sets to general convex sets, plays a special role in a simple and...

Low Mach number equation sets approximate the equations of motion of a compressible fluid by filtering out the sound waves, which allows the system to evolve on the advective rather than the acoustic time scale. Depending on the degree of...

We consider the Dirichlet problem for a strongly-coupled nonlocal system of equations motivated by peridynamics, a model in continuum mechanics. The strain energy densities involve the magnitude of projected "directional" difference quotients of the...

In this talk I will present the personal journey that the study of mathematics has allowed me to travel so far. It begins with a typical academic career, focused on research in applied mathematics, and specifically the mathematics related to the...

The talk will be devoted to a simple introduction to stochastic PDEs (SPDEs) driven by Wiener processes, including both forward and backward (in time) cases. Some motivations, as well as the duality relationship between forward and backward SPDEs...

The purpose of this summer school is to introduce students in applied and computational mathematics to neural networks and deep learning. It will feature three days of lectures and hands-on tutorials, and be followed by a one-day workshop showcasing...