Publications related to Metric Learning with Rank and Sparsity Constraints

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

EPFL2024

Towards Trustworthy Deep Learning for Image Reconstruction

Alexis Marie Frederic Goujon

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

EPFL2024

Understanding generalization and robustness in modern deep learning

Maksym Andriushchenko

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

EPFL2024

Unlabeled Principal Component Analysis and Matrix Completion

Liangzu Peng, Yunzhen Yao

We introduce robust principal component analysis from a data matrix in which the entries of its columns have been corrupted by permutations, termed Unlabeled Principal Component Analysis (UPCA). Using algebraic geometry, we establish that UPCA is a well-de ...

Microtome Publishing2024

Hardware Implementation of Digital Signal Processing Algorithms for Programmable Epilepsy Control Systems

Keyvan Farhang Razi

Epilepsy is a common chronic neurological disorder that causes recurring seizures and affects more than 50 million people worldwide. Implantable medical devices (IMDs) are regarded as effective tools to cure patients who suffer from refractory epilepsy. Se ...

EPFL2023

From Nano to Macro An overview of the IEEE Bio Image and Signal Processing Technical Committee

Michael Stefan Daniel Liebling, Michaël Unser, Dimitri Nestor Alice Van De Ville

The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the IEEE Signal Processing Society (SPS) promotes activities within the broad technical field of biomedical image and signal processing. Areas of interest include medical and biological ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Transfer learning application of self-supervised learning in ARPES

Gabriel Aeppli

There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lase ...

IOP Publishing Ltd2023

Sparse Autoencoders for Speech Modeling and Recognition

Selen Hande Kabil

Speech recognition-based applications upon the advancements in artificial intelligence play an essential role to transform most aspects of modern life. However, speech recognition in real-life conditions (e.g., in the presence of overlapping speech, varyin ...

EPFL2023

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

Alexis Berne, Gionata Ghiggi, Anne-Claire Marie Billault--Roux

The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectr ...

COPERNICUS GESELLSCHAFT MBH2023

Vibrational NDT with Under-sampled Data through Physics-informed Neural Networks

Olga Fink

The vibrational response of solid materials and structural components is substantially governed by their mechanical and geometrical properties. Low-frequency vibrations and modal frequencies are sensitive to global geometrical deviations, while high-freque ...

2023

Real-time actuation of a dielectric elastomer actuator neuroprosthesis for facial paralysis

Yves Perriard, Yoan René Cyrille Civet, Thomas Guillaume Martinez, Stefania Maria Aliki Konstantinidi, Armando Matthieu Walter, Amine Benouhiba

Facial paralysis is a highly burdening condition, resulting in a patient's inability to move his mimic musculature on one or both sides of his face. This condition compromises the patient's communication and facial expressions, and thus dramatically reduce ...

2023

A Visionary Look at the Security of Reconfigurable Cloud Computing

Francesco Regazzoni, Mirjana Stojilovic

Field-programmable gate arrays (FPGAs) have become critical components in many cloud computing platforms. These devices possess the fine-grained parallelism and specialization needed to accelerate applications ranging from machine learning to networking an ...

2023

Finding information-rich electrocardiographic biomarkers to characterize atrial fibrillation

Anna Mary Mc Cann

Atrial fibrillation (AF) is the most common cardiac arrhythmia; it will affect one in four adults worldwide in their lifetime. AF has serious consequences, including drastically increased risk of stroke. Catheter ablation surgery is an established treatmen ...

EPFL2023

Image scanning microscopy with a doughnut beam: signal strength and integrated intensity

Giorgio Tortarolo

We discuss the effects of image scanning microscopy using doughnut beam illumination on the properties of signal strength and integrated intensity. Doughnut beam illumination can give better optical sectioning and background rejection than Airy disk illumi ...

Optica Publishing Group2023

Impact of Video Processing Operations in Deepfake Detection

Touradj Ebrahimi, Yuhang Lu

The detection of digital face manipulation in video has attracted extensive attention due to the increased risk to public trust. To counteract the malicious usage of such techniques, deep learning-based deepfake detection methods have been developed and ha ...

2023

Fairness and Explainability in Clustering Problems

Xinrui Jia

In this thesis we present and analyze approximation algorithms for three different clustering problems. The formulations of these problems are motivated by fairness and explainability considerations, two issues that have recently received attention in the ...

EPFL2023

(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability

Scott William Pesme, Nicolas Henri Bernard Flammarion, Mathieu Even

In this paper, we investigate the impact of stochasticity and large stepsizes on the implicit regularisation of gradient descent (GD) and stochastic gradient descent (SGD) over diagonal linear networks. We prove the convergence of GD and SGD with macroscop ...

2023

Quantization for Decentralized Learning Under Subspace Constraints

Ali H. Sayed, Roula Nassif, Stefan Vlaski, Marco Carpentiero

In this article, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained fo ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Towards learning-based denoising of light fields

Touradj Ebrahimi, Michela Testolina, Tomás Soares De Carvalho Feith

In recent years, new emerging immersive imaging modalities, e.g. light fields, have been receiving growing attention, becoming increasingly widespread over the years. Light fields are often captured through multi-camera arrays or plenoptic cameras, with th ...

2023

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Mehmet Fatih Sahin

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023