Publications related to Approximate Sparsity Pattern Recovery: Information-Theoretic Lower Bounds

Related publications (32)

Eflop: a Sparsity-aware Metric for Evaluating Computational Cost in Spiking and Non-spiking Neural Networks

Deploying energy-efficient deep neural networks on energy-constrained edge devices is an important research topic in both machine learning and circuit design communities. Both artificial neural networks (ANNs) and spiking neural networks (SNNs) have been p ...

IOP Publishing Ltd2025

Shuffled multi-channel sparse signal recovery

Benjamin Bejar Haro

Mismatches between samples and their respective channel or target commonly arise in several real-world applications. For instance, whole-brain calcium imaging of freely moving organisms, multiple-target tracking or multi-person contactless vital sign monit ...

2024

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

Pakshal Narendra Bohra

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

Quantization for Decentralized Learning Under Subspace Constraints

Ali H. Sayed, Stefan Vlaski, Roula Nassif

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

An Ultra Low-Noise Micropower PPG Sensor

Antonino Caizzone

Nowadays, efficient and remote health monitoring is becoming increasingly important, given both the ageing of population and the combined action of an increase in obesity level and cardiovascular diseases. The healthcare industry is becoming more reliant o ...

EPFL2020

Sparse and Parametric Modeling with Applications to Acoustics and Audio

Helena Peic Tukuljac

Recent advances in signal processing, machine learning and deep learning with sparse intrinsic structure of data have paved the path for solving inverse problems in acoustics and audio. The main task of this thesis was to bridge the gap between the powerfu ...

EPFL2020

Deconvolution of Sustained Neural Activity From Large-Scale Calcium Imaging Data

Dimitri Nestor Alice Van De Ville, Fikret Isik Karahanoglu, Younes Farouj

Recent technological advances in light-sheet microscopy make it possible to perform whole-brain functional imaging at the cellular level with the use of Ca2+ indicators. The outstanding spatial extent and resolution of this type of data open unique opportu ...

Institute of Electrical and Electronics Engineers2020

A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms

Mikhail Kapralov, Amir Zandieh

Reconstructing continuous signals based on a small number of discrete samples is a fundamental problem across science and engineering. We are often interested in signals with "simple" Fourier structure - e.g., those involving frequencies within a bounded r ...

ASSOC COMPUTING MACHINERY2019

Sparsest Univariate Learning Models Under Lipschitz Constraint

Michaël Unser, Shayan Aziznejad, Thomas Jean Debarre

Beside the minimizationof the prediction error, two of the most desirable properties of a regression scheme are stability and interpretability. Driven by these principles, we propose continuous-domain formulations for one-dimensional regression problems. I ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Analysis of photosensor frontends for biomedical applications: a focus on noise

Paul Stanislas Gosselin

Photosensing has proven to be highly useful in biomedical research and medical care. Being able to detect and quantify the light emitted by fluorescent or bioluminescent reporters has significantly participated in the development of new cancer treatments a ...

EPFL2019

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

Thomas Jean Debarre

The goal of this thesis is to study continuous-domain inverse problems for the reconstruction of sparse signals and to develop efficient algorithms to solve such problems computationally. The task is to recover a signal of interest as a continuous function ...

EPFL2022

Joint Sparsity with Partially Known Support and Application to Ultrasound Imaging

Jean-Philippe Thiran, Yves Wiaux, Adrien Georges Jean Besson, Dimitris Perdios

We investigate the benefits of known partial support for the recovery of joint-sparse signals and demonstrate that it is advantageous in terms of recovery performance for both rank-blind and rank-aware algorithms. We suggest extensions of several joint-spa ...

IEEE Institute of Electrical and Electronics Engineers2019

Engineering Parallel Transmit/Receive Radio-Frequency Coil Arrays for Human Brain MRI at 7 Tesla

Jérémie Daniel Clément

Magnetic resonance imaging is widely used in medical diagnosis to obtain anatomical details of the human body in a non-invasive way. Clinical MR scanners typically operate at a static magnetic field strength (B0) of 1.5T or 3T. However, going to higher fie ...

EPFL2019

Finite Bit Quantization For Decentralized Learning Under Subspace Constraints

Ali H. Sayed, Stefan Vlaski, Roula Nassif

In this paper, 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 form ...

IEEE2022

Fast high-resolution brain metabolite mapping on a clinical 3T MRI by accelerated H-1-FID-MRSI and low-rank constrained reconstruction

Dimitri Nestor Alice Van De Ville, François Lazeyras, Michel Kocher, Matthieu Guerquin-Kern, Sébastien Courvoisier, Jeffrey Adam Kasten

Purpose: Epitomizing the advantages of ultra short echo time and no chemical shift displacement error, high-resolution-free induction decay magnetic resonance spectroscopic imaging (FID-MRSI) sequences have proven to be highly effective in providing unbias ...

WILEY2019

An Efficient Signal-to-noise Approximation for Eccentric Inspiraling Binaries

Alexandra Shelest

Eccentricity has emerged as a potentially useful tool for helping to identify the origin of black hole mergers. However, eccentric templates can be computationally very expensive owing to the large number of harmonics, making statistical analyses to distin ...

IOP Publishing Ltd2022

How Does Pre-Trained Wav2Vec 2.0 Perform On Domain-Shifted Asr? An Extensive Benchmark On Air Traffic Control Communications

Petr Motlicek, Juan Pablo Zuluaga Gomez

Recent work on self-supervised pre-training focus on leveraging large-scale unlabeled speech data to build robust end-to-end (E2E) acoustic models (AM) that can be later fine-tuned on downstream tasks e.g., automatic speech recognition (ASR). Yet, few work ...

IEEE2022

Sparse Sampling for Inverse Problems With Tensors

Guillermo Ortiz Jimenez

We consider the problem of designing sparse sampling strategies for multidomain signals, which can be represented using tensors that admit a known multilinear decomposition. We leverage the multidomain structure of tensor signals and propose to acquire sam ...

2019

Streaming From a Moving Platform with Real-Time and Playback Distortion Constraints

Laura Toni, Giuseppe Cocco

Video streaming from remotely controlled moving platforms such as drones have stringent constraints in terms of delay. In some applications such videos have to provide real-time visual feedback to the pilot with an acceptable distortion while satisfying hi ...

IEEE2019

Learning of robust spectral graph dictionaries for distributed processing

Pascal Frossard, Ntorina Thanou

We consider the problem of distributed representation of signals in sensor networks, where sensors exchange quantized information with their neighbors. The signals of interest are assumed to have a sparse representation with spectral graph dictionaries. We ...

SPRINGEROPEN2018