Phase Retrieval: From Computational Imaging to Machine Learning: A tutorial

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The escalating demands of artificial intelligence have underscored the limitations of traditional electronic computing, particularly in terms of power consumption and scalability. Optical computing offers a promising alternative, providing advantages in sp ...

Efficient deployment of resource-intensive transformers on edge devices necessitates cross-stack optimization. We thus study the interrelation between structured pruning and systolic acceleration, matching the size of pruned blocks with the systolic array ...

DUSt3R introduced a novel paradigm in geometric computer vision by proposing a model that can provide dense and unconstrained Stereo 3D Reconstruction of arbitrary image collections with no prior information about camera calibration nor viewpoint poses. Un ...

This study examines the spatial and visual organisation of tombs in the post-Roman Berenike located in Egypt's Eastern Desert. Archaeological surveys, remote sensing, geophysical methods, excavations and GIS-based analyses are used for a comprehensive unde ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limi ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

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 ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, ...

Phase Retrieval: From Computational Imaging to Machine Learning: A tutorial

Chattez avec Graph Search

Programming Nonlinear Computation with Nonlinear and Linear Optics

Systolic Arrays and Structured Pruning Co-design for Efficient Transformers in Edge Systems

MUSt3R: Multi-view Network for Stereo 3D Reconstruction

A Multifaceted Spatial Analysis of Tomb Distribution in Blemmyan Berenike (eastern Desert of Egypt)

Robust machine learning for neuroscientific inference

Topics in statistical physics of high-dimensional machine learning

Random matrix methods for high-dimensional machine learning models

Safe Deep Neural Networks

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Machine Learning for Modeling Stock Returns

Partial discharge localization in power transformer tanks using machine learning methods

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

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging