Seminar Details
2024-10-15 (14h) : Newcomers seminars (PhDs)
At Euler building (room a.002)
Organized by Mathematical Engineering
Section 1: Generalized low-rank plus sparse models in angular and spectral differential imaging for exoplanets detection with regularized implicit neural representations
Speaker :
Nicolas Mil-Homens Cavaco (PhD UCLouvain/INMA)
Abstract :
Differential imaging is a widespread technique that involves post-processing images captured by ground-based telescopes during an observing campaign in order to make exoplanets in a distant planetary system directly visible. This technique is based on introducing diversity into the observation process, for example by taking advantage of the Earth's rotation in angular differential imaging (ADI) or by recording many wavelengths in spectral differential imaging (SDI), or a combination of both (ASDI). The effect is to increase the signal-to-noise ratio of exoplanet image features compared to unstructured and non-physical data corruption. Direct imaging of exoplanets with ASDI is nevertheless particularly challenging since an exoplanet is faint compared to its host star and the surrounding data corruption noise. In this context, we propose to develop novel signal representations and inverse problem-solving techniques by incorporating regularized implicit neural representations (INRs), defined as continuous parametric models based on neural network architectures, into dedicated low-rank plus sparse models to address the specific geometric transformations experienced by exoplanets in ASDI and to reduce the interpolation error induced by these transformations. More generally, this work aims at offering innovative solutions for employing INRs in continuous or high-dimensional signal representations for various inverse problems, especially when low-rank, sparse or low-rank plus sparse models are typically employed.
Section 2: Bridging Minds and Movements : Nonlinear Control Models for Human Reaching Movements
Speaker :
Alexandre Thyrion (PhD UCLouvain/INMA)
Abstract :
A large majority of currents research aiming at improving the understanding of the cerebral mechanisms underlying human reaching movements are based on linear approximation of the biomechanics of the body, neglecting completely the impact of the inherent nonlinearities of the system, but allowing the use of linear control models. However, evidence has shown that this simplification, although extremely common, could in many cases be inadequate. This study develops nonlinear control models allowing to study directly the behavior of a more realistic nonlinear model of biomechanics. We also aim to study the underlying hypotheses brought by these new models and their implication on the a priori functioning of the brain. Finally, we will compare the movements produced by the model with experimental observations and give some insights about future research.
Section 3: Computer-assisted analysis of inexact and stochastic first-order optimization methods.
Speaker :
Vernimmen, Pierre
Abstract :
The increasing complexity of large-scale optimization challenges, particularly in the field of machine learning, requires the development of more efficient algorithms. First-order methods have emerged as a preferred choice due to their simplicity and minimal computational requirements; however, their effectiveness can decrease when information is inexact, or if they are subject to stochastic influences. This study aims to improve the Performance Estimation Methodology (PEP) - a robust framework that automates the evaluation of optimization algorithms - to solve these problems in inexact and stochastic environments.
Using PEP, we will examine traditional and new first-order optimization algorithms in scenarios where gradient information is inexact or where randomness affects the decision-making process, a situation frequently encountered in data-driven applications such as machine learning. The main objective is to deepen the theoretical understanding of these algorithms, refine their worst-case performance guarantees and develop improved methods that demonstrate greater reliability and efficiency in real-world applications.
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