All Years Seminars
[INMA] 2022-12-13 (14h) : Riemannian optimization for the tensor rank decomposition
At Euler building(room a.002)
Speaker :
Nick Vannieuwenhoven (KU Leuven)
Abstract : The tensor rank decomposition or canonical polyadic decomposition (CPD) is a generalization of a low-rank matrix factorization from matrices to higher-order tensors. In many applications, multi-dimensional data can be meaningfully approximated by a low-rank CPD. In this talk, I will describe a Riemannian optimization method for approximating a tensor by a low-rank CPD. This is a type of optimization method in which the domain is a smooth manifold, i.e. a curved geometric object. The presented method achieved up to two orders of magnitude improvements in execution time for challenging small-scale dense tensors when compared to state-of-the-art nonlinear least squares solvers.
[INMA] 2022-12-06 (14h) : Chance-constrained problems with integer second-stage recourse decisions
At Core(room B-135)
Speaker :
Enrico Malaguti (University of Bologna)
Abstract : We study a class of chance-constrained two-stage stochastic optimization problems where the second-stage recourse decisions belong to mixed-integer convex sets. Due to the nonconvexity of the second-stage feasible sets, standard decomposition approaches cannot be applied. We develop a provably convergent branch-and-cut scheme that iteratively generates valid inequalities for the convex hull of the second-stage feasible sets, resorting to spatial branching when cutting no longer suffices. We show that this algorithm attains an approximate notion of convergence, whereby the feasible sets are relaxed by some positive tolerance. Computational results on chance-constrained resource planning problems indicate that our implementation of the proposed algorithm is highly effective in solving this class of problems, compared to a naive reformulation tackled with a state-of-the-art MIP solver.
[INMA] 2022-11-29 (14h) : Welcome Seminar
At Euler building (room a.002)
Section 1:Double descent in deep learning: Exploring the impact of common regularization, compression and optimization strategies
Speaker :
Massion, Bastien
Abstract : For a decade, deep neural networks have revolutionized the domain of machine learning and artificial intelligence. The driving force behind this success is their unprecedented generalization abilities. The mechanisms behind this generalization have long been a mystery. Indeed, it defies the classical bias-variance trade-off as deep learning models are highly overparameterized but still avoid overfitting. The double descent phenomenon has been proposed to characterize this behaviour: the generalization error follows the classical U-shape curve in function of the model complexity but decreases again when the number of parameters exceeds the number of training data, reaching the modern learning regime. Many explanations of the phenomenon have since then been proposed, relying on implicit regularization of the model. Yet, a lot of work has still to be done to measure the impact on double descent of several techniques commonly used in deep learning. This work aims to explore 3 aspects of training. First, explicit orthogonal regularization of neural network weights. Then, low-rank matrix factorization of these weights for compression purposes. Finally, the impact of dropout and momentum in model training.
Section 2:Data-driven control of Markovian Jump Linear Systems: a step towards complexity
Speaker :
Adrien Banse (PhD UCLouvain/INMA)
Abstract : Hybrid systems are dynamical systems whose dynamics is characterised by both continuous and discrete behaviours. Their hybrid characteristic makes these systems notoriously extremely hard to control and analyse. However, they often generate rich data (harvested from cameras, lidars, etc.), to which the engineer has access in large quantities. In my thesis, I plan to tackle the following question: how to control such complex systems in a data-driven fashion? It means, from datasets containing observations of a hybrid system, what can we say about its stability, or how can we design a controller to stabilise it? In particular, Markovian Jump Linear Systems (MJLS), while popular tools for modelling complex hybrid systems, have remained untouched in the framework of data-driven control. My goal, in four years from now, is to prove theorems providing firm probabilistic guarantees on the stability and stabilisation of MJLS. I will proceed in an incremental way, starting by considering a simpler system that I have already studied, namely constrained switching linear systems. I also plan to provide a proof of concept by applying my methods to networked controlled systems, a known application of MJLS
Section 3:Investigation of the neural circuitry involved in the control of movements
Speaker :
Astrid Doyen (PhD UCLouvain/INMA)
Abstract : Reaching movements are movements that we perform thousands of times in everyday life. Computationally, the control of these movements can be represented by the Optimal Feedback Control model, which explains most human behaviours. Among those behaviours, one can mention motor adaptation, i.e., the ability of humans to adapt to new tools or environments, and online change in control policy, i.e., the ability of humans to rapidly change the command sent to the arm following a change in task or movement parameters during the movement. In the Optimal Feedback Control model, these behaviours are attributed to different structures, but it is not known yet if it is also the case biologically. Therefore, this research aims to highlight experimentally potential interferences between both mechanisms to give insight into the biological structures involved in these behaviours and to particularly investigate the role in both tasks of the basal ganglia, a group of subcortical nuclei known to be involved in learning and selection of the most appropriate motor behavioural programs. The Optimal Feedback Control model can then be extended by considering the experimental results.
[INMA] 2022-11-22 (14h) : The fascinating and frightening face of nanoelectronics
At Euler building (room A.002)
Speaker :
Jean-Pierre Raskin (UCLouvain, ICTEAM/ELEN)
Abstract :
Nanotechnology is revolutionizing the way we communicate, consume, and think. The manufacture and characterization of these nanoscale objects are real challenges for scientists and industry. In the field of nanoelectronics the characteristic sizes of transistors are constantly decreasing and with this reduction in size the structural defects in and between the materials as well as the residual mechanical stresses within the thin films, to name a few, greatly limit performance of some basic electronic components. The scientific community is working to find solutions to these problems. However, these same technological limitations can be exploited in an original way and become real sources of innovation in other fields of application. Two examples will be presented: (i) the interest of interface defects in the production of a high performance silicon substrate essential for the integration of the high frequency electronics necessary for any wireless object, (ii) the exploitation of internal stresses in thin films for the development of mechanical test laboratories on silicon chips in order to explore the electromechanical behavior of materials at the nanometer scale. The beauty of these technological innovations cannot hide a much less pleasing environmental and social reality. The use of these advanced, complex, energy-intensive technologies that require a lot of critical and toxic materials must be considered with much more awareness. Engineers and scientists must embrace the complexity of these societal challenges by adopting a holistic approach. This paradigm shift must be taught to future engineers so that they contribute to the development of a more sustainable and fair world.
[INMA] 2022-11-15 (14h) : Designing optimal masks for a multi-object spectrometer
At Euler building (room A.002)
Speaker :
Juan José Salazar (Universidad de la Laguna)
Abstract :
This paper concerns a new optimization problem arising in the management of a multi-object spectrometer with a configurable slit unit. The field of view of the spectrograph is divided into contiguous and parallel spatial bands, each one associated with two opposite sliding metal bars that can be positioned to observe one astronomical object. Thus several objects can be analyzed simultaneously within a configuration of the bars called a mask. Due to the high demand from astronomers, pointing the spectrograph’s field of view to the sky, rotating it, and selecting the objects to conform a mask is a crucial optimization problem for the efficient use of the spectrometer. The paper describes this problem, presents a Mixed Integer Linear Programming formulation for the case where the rotation angle is fixed, presents a non-convex formulation for the case where the rotation angle is unfixed, describes a heuristic approach for the general problem, and discusses computational results on real-world and randomly-generated instances.
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