All Years Seminars
[INMA] 2018-11-13 (14:00) : Parametric model order reduction and interpolation on matrix manifolds as a tool
At Euler building (room A.002)
Speaker :
Nguyen, Thanh Son
Abstract :
Large-scale control systems appear frequently as mathematical
models for many practical fields such as heat conduction,
electrical circuits. Simulation of such systems requires
solving equations whose order can reach dozen of thousands.
Besides, in many cases, these systems depend on parameter which
makes the simulation more challenging. In this talk, first we will
give an introduction to model order reduction of
parameter-dependent linear time-invariant systems as well as a
review of interpolation-based methods. Then we will explain why in
some cases, we have to do it in the framework of interpolation on
matrix manifolds. Finally, we will present some numerical examples
for illustrative purpose.
[INMA] 2018-11-06 (14:00) : Welcome Seminar #2
At Euler building (room A.002)
Speaker :
New PhD students in INMA
Abstract :
In this seminar, new PhD students in INMA will introduce themselves and their research topic: "Compressive Learning : learning from 'too much' data" (Vincent Schellekens); "Signal processing for direct imaging of stellar systems" (Benoît Pairet); "Optimal interference nulling for large arrays of coupled antennas" (Valentin Hamaide); "Algorithms for smart content marketing" (Mridul Seth).
[INMA] 2018-10-23 (14:00) : Interferometry and convex programming
At Euler building (room A.002)
Speaker :
Demanet, Laurent
Abstract :
A number of hard estimation questions in science and engineering can be expressed as recovering a rank-1 matrix from linear measurements. One example that I will cover in this talk is "interferometry", a trick in signal processing and imaging consisting in taking cross-correlations to attenuate incoherent noise. I show that recovery for inverse problems involving interferometric combinations is sometimes possible with varying levels of convex relaxations, and depends on the underlying graph of measurements being an expander. I also show an example of "interferometric thinking" to blind deconvolution, where it enables the design of new sparse regularizers.
[INMA] 2018-10-16 (14:00) : Welcome Seminar #1
At Euler building (room A.002)
Speaker :
New PhD students in INMA
Abstract :
In this seminar, new PhD students in INMA will introduce themselves and their research topic: "Nonnegative matrix factorization in infinite-dimensional feature spaces : a parameterized approach" (Cécile Hautecoeur); "Analysis and Control of Interconnected Systems" (Ayoub Ben Ayed); "Decentralized optimization in open multi-agent systems" (Charles Monnoyer); "MILP-based Algorithm for the Global Solution of Economic Dispatch Problems with Valve-Point Effects" (Loïc Van Hoorebeeck).
[INMA] 2018-10-09 (14:00) : Fast and invariant learning for Independent Component Analysis
At Euler building (room A.002)
Speaker :
Jean-François Cardoso (CNRS - INRIA)
,
and
Ablin, Pierre
Abstract :
Independent Component Analysis (ICA) is a widely used unsupervised data exploration technique. It models a set of observed signals as a linear mixtures of statistically independent sources and aims at recovering blindly those underlying sources. Here `blind' means that sources are recovered from the signals by applying a separating matrix which is totally inconstrained, making ICA applicable in a large variety of tasks.
After a brief introduction, we stress the multiplicative structure of the ICA problem: the parameter space is the multiplicative group of GL(n) of square invertible matrices. Learning algorithms which exploit this structure are `equivariant' and are `rewarded' for doing so in terms implementation, optimization and performance, as we shall see.
In a second part, we introduce a fast quasi-Newton equivariant algorithm for ICA, the Preconditioned ICA for Real Data (Picard) algorithm. It exploits the specific structure of the problem to compute cheap Hessian approximations, and then refines them using L-BFGS, a classical optimization algorithm. It shows state of the art convergence speed when applied to real datasets. Interestingly, it can be straightforwardly constrained to work on the rotation manifold, a constraint often imposed in ICA.
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