All Years Seminars
[INMA] 2017-12-12 (14:00) : Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions
At Euler building (room A.002)
Speaker :
Michaël Fanuel (UCL)
Abstract :
Dimensionality reduction of data is often an important preprocessing step in statistics or machine learning. Several non-linear techniques exist, for instance, Diffusion Maps or, more generally, manifold learning. In this context, we will discuss a recent proposal to reduce the dimension of Euclidean data by relying on methods developed for graph embedding. More specifically, we discuss a numerical method phrased as a Semi-Definite Program for estimating a distance (semi-metric) on the data cloud. As an interesting feature, this positive semi-definite "matrix" can be extended to a "kernel" function thanks to an out-of-sample formula. Indeed, if any new data point arises, it may also be embedded at a low computational cost thanks to the extension formula. Inspired by this property, we will also briefly discuss an infinite dimensional analogue of this discrete problem in the context of machine learning.
[INMA] 2017-12-06 (17:00) : Design of soft machines
At BARB92
Speaker :
Mattia Gazzola (Mechanical Science and Engineering, UIUC National Center for Supercomputing Applications)
Abstract :
[This is a joint seminar with IMMC. Note the usual date and place.]
We introduce an approach based on minimal theoretical modeling, direct numerical simulations and evolutionary optimization for the characterization and design of arbitrary muscoloskeletal architectures. Applications range from slithering and swimming biolocomotion strategies to artificial muscles and bio-hybrid systems.
[INMA] 2017-11-21 (14:00) : Search for new rare physics processes: towards a "deeper" understanding of LHC data?
At Euler building (room A.002)
Speaker :
Christophe Delaere (UCL)
Abstract :
The Large Hadron Collider (LHC) allows us to probe sub-protonic scales where new elementary particles and / or new fundamental interactions could appear. The recent observation of the Higgs particle in 2012 was only made possible through the use of advanced multivariate analysis techniques. While more data is being collected, the collaboration are developing new approaches to data analysis, both for the low-level "event reconstruction" and for the high-level signal extraction, using deep learning when appropriate. In this seminar, we will try to better define what "observation" means and illustrate with examples the data analysis techniques explored to observe possible new physics phenomena at the LHC in the next decade.
[INMA] 2017-11-14 (14:00) : Improving learning in robotics by exploiting the structure and geometry of the data: application to prosthetic hands
At Euler building (room A.002)
Speaker :
Noémie Jaquier (Ecole Polytechnique Fédérale de Lausanne)
Abstract :
In many sensing and control applications, data are represented in the form of multidimensional arrays with particular geometric properties such as symmetries. Considering the underlying structure and geometry of the data can be beneficial in many robotics applications. This is particularly important in the context of robot learning from demonstration, when a small set of demonstrations is available, i.e. when the robots are required to reproduce tasks with generalization to new situations.
I will present different regression techniques and their extension to models that can efficiently exploit the specific structure and geometry of the acquired datapoints. The approaches will be illustrated in the application of controlling a prosthetic hand using tactile and electromyography to predict hand movements.
[INMA] 2017-11-07 (14:00) : Integration Methods and Accelerated Optimization Algorithms
At Euler building (room A.002)
Speaker :
Damien Scieur (Samsung SAIL Montreal)
Abstract :
We show that accelerated optimization methods can be seen as particular instances of multi-step integration schemes from numerical analysis, applied to the gradient flow equation. In comparison with recent advances in this vein, the differential equation considered here is the basic gradient flow and we show that multi-step schemes allow integration of this differential equation using larger step sizes, thus intuitively explaining acceleration results.
Seminars
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