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A scalable algorithm for radio-interferometric imaging

Publications associées (31)

Robust design using multiobjective optimisation and artificial neural networks with application to a heat pump radial compressor

Jürg Alexander Schiffmann, Cyril Picard, Soheyl Massoudi

Although robustness is an important consideration to guarantee the performance of designs under deviation, systems are often engineered by evaluating their performance exclusively at nominal conditions. Robustness is sometimes evaluated a posteriori throug ...

2022

Learning to sample in Cartesian MRI

Thomas Sanchez

Magnetic Resonance Imaging (MRI) is a non-invasive, non-ionizing imaging modality with unmatched soft tissue contrast. However, compared to imaging methods like X-ray radiography, MRI suffers from long scanning times, due to its inherently sequential acqui ...

EPFL2022

Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions

Jean-Philippe Thiran, Tobias Kober, Elda Fischi Gomez, Alessandro Daducci, Tom Hilbert, Elena Beanato, Gabriel Girard, Julia Brügger, Gian Franco Piredda, Friedhelm Christoph Hummel, Takuya Morishita, Maximilian Jonas Wessel, Philipp Johannes Koch, Erick Jorge Canales Rodriguez, Marco Pizzolato, Andéol Geoffroy Cadic-Melchior, Thomas Yu, Chang-Hyun Park

Quantitative magnetic resonance imaging (qMRI) can increase the specificity and sensitivity of conventional weighted MRI to underlying pathology by comparing meaningful physical or chemical parameters, measured in physical units, with normative values acqu ...

2022

Block-Sparse Adversarial Attack To Fool Transformer-Based Text Classifiers

Pascal Frossard, Seyedeh Sahar Sadrizadeh

Recently, it has been shown that, in spite of the significant performance of deep neural networks in different fields, those are vulnerable to adversarial examples. In this paper, we propose a gradient-based adversarial attack against transformer-based tex ...

IEEE2022

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Patrick Thiran, Saber Salehkaleybar, Negar Kiyavash

We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with

1\le\alpha\le2

which holds in a wide range of applications in machine learning and signal processing. This conditio ...

NeurIPS2022

Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation

Jean-Philippe Thiran, Manana Lortkipanidze, Guillaume Marc Georges Vray, Seyedbehzad Bozorgtabar, Devavrat Tomar

Despite the successes of deep neural networks on many challenging vision tasks, they often fail to generalize to new test domains that are not distributed identically to the training data. The domain adaptation becomes more challenging for cross-modality m ...

Institute of Electrical and Electronics Engineers2021

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Ahmet Alacaoglu

Stochastic gradient descent (SGD) and randomized coordinate descent (RCD) are two of the workhorses for training modern automated decision systems. Intriguingly, convergence properties of these methods are not well-established as we move away from the spec ...

EPFL2021

Optimization methods for collaborative learning

Sai Praneeth Reddy Karimireddy

A traditional machine learning pipeline involves collecting massive amounts of data centrally on a server and training models to fit the data. However, increasing concerns about the privacy and security of user's data, combined with the sheer growth in the ...

EPFL2021

Ultrasound Imaging: From Physical Modeling to Deep Learning

Dimitris Perdios

Among the medical imaging modalities, ultrasound (US) imaging is one of the safest, most widespread, and least expensive method used in medical diagnosis. In the past decades, several technological advances enabled the advent of ultrafast US imaging, an ac ...

EPFL2021

Computational Inverse Design of Surface-based Inflatables

Mark Pauly, Francis Julian Panetta, Florin Isvoranu, Tian Chen

We present a computational inverse design method for a new class of surface-based inflatable structure. Our deployable structures are fabricated by fusing together two layers of inextensible sheet material along carefully selected curves. The fusing curves ...

2021

Increasing Superstructure Optimization Capacity Through Self-Learning Surrogate Models

François Maréchal, Ivan Daniel Kantor, Julia Granacher

Simulation-based optimization models are widely applied to find optimal operating conditions of processes. Often, computational challenges arise from model complexity, making the generation of reliable design solutions difficult. We propose an algorithm fo ...

2021