Seminar Details
2021-05-25 (14h) : Master Students' presentations (2/2)
At MS Teams
Organized by Mathematical Engineering
Section 1: Disciplined Convex Programming
Speaker :
Aurélie de Borman
Abstract :
Disciplined convex programming is a methodology for constructing convex optimization models. While convex programming requires a certain level of expertise, disciplined convex programming aims at lowering the expertise barrier. The methodology proposes an extensible collection of functions and sets to be combined according to a set of conventions for constructing convex models. In turn, much of the work to solve the models is automated.
Section 2: Large deviations theory
Speaker :
Sylvie Van Schendel
Abstract :
Large deviations theory deals with the probabilities of rare events that decay
exponentially. I will start my talk with a basic example, based on the sample mean of
normally distributed i.i.d. random variables. This will lead us to the definition of the Large
Deviations Principle (LDP). Then, I will talk about 3 methods that can be used to show
that a random variable satisfies an LDP. Some main theorems of LD theory will be
presented. And finally, we will have a look at an application of Large Deviations Theory.
Section 3: Neural Networks: they work but one does not know why.
Speaker :
Pierre Veldeman
Abstract :
Neural Networks are powerful tools (for example in image or speech recognition) but it is still difficult to interpret them and to understand clearly how they work, or to be able to distinguish the good architectures from the bad ones. In this presentation, we will first briefly introduce the neural networks, then we will highlight some of their astonishing properties and show that common measures of performance do not take those strange features into account.