Active Subdivision Surfaces for the Semiautomatic Segmentation of Biomedical Volumes

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The monumental progress in the development of machine learning models has led to a plethora of applications with transformative effects in engineering and science. This has also turned the attention of the research community towards the pursuit of construc ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...

Deep Neural Networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation. To address this problem, we propose a general class of Hamiltonian DNNs (H-DNNs) that stem from the discretiz ...

During the past two decades, educating students about sustainability issues has been a concern when designing engineering schools' curricula. One could think that nowadays students are trained to take into account the multiple dimensions of sustainability ...

Neural decoding of the visual system is a subject of research interest, both to understand how the visual system works and to be able to use this knowledge in areas, such as computer vision or brain-computer interfaces. Spike-based decoding is often used, ...

In this paper we explore deep learning models to monitor longitudinal liveability changes in Dutch cities at the neighbourhood level. Our liveability reference data is defined by a country-wise yearly survey based on a set of indicators combined into a liv ...

We develop a principled approach to end-to-end learning in stochastic optimization. First, we show that the standard end-to-end learning algorithm admits a Bayesian interpretation and trains a posterior Bayes action map. Building on the insights of this an ...

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The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

The minimization of a data-fidelity term and an additive regularization functional gives rise to a powerful framework for supervised learning. In this paper, we present a unifying regularization functional that depends on an operator L\documentclass[12pt]{ ...

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We present a statistical framework to benchmark the performance of reconstruction algorithms for linear inverse problems, in particular, neural-network-based methods that require large quantities of training data. We generate synthetic signals as realizati ...

In this thesis, we explore techniques for addressing the communication bottleneck in data-parallel distributed training of deep learning models. We investigate algorithms that either reduce the size of the messages that are exchanged between workers, or th ...

Group testing is a technique that can reduce the number of tests needed to identify infected members in a population, by pooling together multiple diagnostic samples. Despite the variety and importance of prior results, traditional work on group testing ha ...

Active Subdivision Surfaces for the Semiautomatic Segmentation of Biomedical Volumes

Chattez avec Graph Search

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Scalable constrained optimization

Deep Learning Generalization with Limited and Noisy Labels

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

Exploring journeys from sustainable design to design for sustainability

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

Time Series Analysis of Urban Liveability

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Polynomial-time universality and limitations of deep learning

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

From Kernel Methods to Neural Networks: A Unifying Variational Formulation

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

Communication-efficient distributed training of machine learning models

Community-Aware Group Testing