All Years Seminars
[INMA] 2024-03-19 (14h) : Harnessing AI: Solutions for Climate Change and Learning Innovation
At Euler building (room A.002)
Speaker :
Maureen Heymans (Google)
Abstract :
The recent explosion of AI has led to remarkable advancements and a wave of innovative applications. In this talk, we will explore the immense potential of AI to address two critical challenges facing our world: education and climate change. We will quickly discuss the transformative potential of GenAI in revolutionizing education, from providing personalized learning experiences, empowering teachers with useful tools, and bridging gaps in access to quality education.
We will also uncover how AI can be a powerful force in the fight against climate change and learn about AI-driven solutions for predicting climate scenarios and mitigating risks, helping individuals make sustainable decisions, and building a more sustainable future for our planet.
[INMA] 2024-03-12 (14h) : Active Monitoring of Large Scale Phenomena
At Euler building (room A.002)
Speaker :
Emanuele Garone (ULB)
Abstract :
The capability to acquire relevant measurements is a key aspect for the monitoring of phenomena of any kind. However, measurements come at a cost. In this seminar we will focus on some recent results on the “smart measurement” for those systems where it is unrealistic “to measure everything at every time” and decisions on “what to measure and when”. Case studies coming from precision agriculture will be shown and discussed.
[INMA] 2024-03-05 (14h) : Learning to Reconstruct Signals From Binary Measurements
At Euler building (room A.002)
Speaker :
Jacques, Laurent
Abstract :
Recent advances in unsupervised learning have highlighted the possibility of learning to reconstruct signals from noisy and incomplete linear measurements alone. These methods play a key role in medical and scientific imaging and sensing, where ground truth data is often scarce or difficult to obtain. However, in practice measurements are not only noisy and incomplete but also quantized. Here we explore the extreme case of learning from binary observations and provide necessary and sufficient conditions on the number of measurements required for identifying a set of signals from incomplete binary data. Our results are complementary to existing bounds on signal recovery from binary measurements. Furthermore, we introduce a novel self-supervised learning approach that only requires binary data for training. We demonstrate in a series of experiments with real datasets that our approach is on par with supervised learning and outperforms sparse reconstruction methods with a fixed wavelet basis by a large margin. This is joint work with Julián Tachella.
[INMA] 2024-02-27 (14h) : Numerical linear algebra for weather forecasting
At Euler building (room A.002)
Speaker :
Jemima Tabeart (TU Eindhoven)
Abstract :
The quality of a weather forecast is strongly determined by the accuracy of the initial condition. Data assimilation methods allow us to combine prior forecast information with new measurements in order to obtain the best estimate of the true initial condition. However, many of these approaches require the solution an enormous least-squares problem. In this talk I will discuss some mathematical and computational challenges associated with data assimilation for numerical weather prediction, and show how structure-exploiting numerical linear algebra approaches can lead to theoretical and computational improvements. This work was conducted together with John Pearson (Edinburgh) and Davide Palitta (Bologna).
[INMA] 2024-02-20 (14h) : Newcomers seminars
At Euler building (room a.002)
Section 1:Analysis and development of almost-linear time Laplacian solvers for finite element problems.
Speaker :
Christophe Heneffe (PhD UCLouvain/INMA)
Abstract : Many applications require solving Laplacian systems as fast as possible. It is the case, for example, with finite element simulations. There exist many methods to solve such systems that work well in practice, but they don't enjoy strong theoretical guarantees except in very specific cases. In the last decades, many theoretical algorithms have been created which allows to solve any Laplacian systems in almost linear time. However, most have never been implemented in practice. These algorithms are very general since they work for any Laplacian system. Adapting them by taking advantage of the properties of the finite element meshes could lead to very efficient solvers for FEM problems. The purpose of this project is the development, analysis and implementation of almost-linear time algorithms specifically adapted to solve FEM problems.
Section 2:Efficient, Complete G-Invariance for G-Equivariant Networks via Algorithmic reduction.
Speaker :
Mataigne, Simon
Abstract : Group-Equivariant Convolutional Neural Networks (G-CNNs) generalize the translation-equivariance of traditional CNNs to group-equivariance, using more general symmetry transformations such as rotation for weight tying. For tasks such as classification, such transformations are removed at the end of the network, to achieve group-invariance, typically by taking a maximum over the group. While this is indeed invariant, it is excessively so; two inputs that are non-identical up to group action can yield the same output, resulting in a general lack of robustness to adversarial attacks. Sanborn and Miolane (2023) proposed an alternative method for achieving invariance without loss of signal structure, called the $G$-triple correlation ($G$-TC). While this method yields demonstrable gains in accuracy and robustness, it comes with a significant increase in computational cost. In this paper, we introduce a new invariant layer based on the Fourier transform of the $G$-TC: the $G$-bispectrum. Operating in Fourier space allows us to significantly reduce the computational cost. Our main theoretical result provides a reduction of the $G$-bispectrum that conserves the selective invariance of the $G$-TC, while only requiring $\mathcal{O}(|G|)$ coefficients. In a suite of experiments, we demonstrate that our approach retains all of the advantages of the $G$-TC, while significantly reducing the computational cost
Section 3:An event-triggered data-driven predictive control
Speaker :
Amir Mehrnoosh (PhD UCLouvain/INMA)
Abstract : We develop a data-driven model predictive control (MPC) design procedure to control unknown linear time-invariant systems. This algorithm only requires measured input-output data to drive the system to the reference signal. We add filters on desired inputs and outputs in the cost function to improve the transient response. Moreover, the Hankel matrices are updated online based on a multi-step event-triggered MPC scheme to deal with the uncertainties. This also reduces the computational cost and balances it with the closed-loop performance. Simulation results illustrate the effectiveness of the proposed approach.
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