Publication
3D Reconstruction of Optical Diffraction Tomography Based on a Neural Network Model
Publications associées (121)
Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks
Romain Christophe Rémy Fleury, Janez Rus
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 ...
2024Deep Learning Generalization with Limited and Noisy Labels
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 ...
EPFL20233D reconstruction of curvilinear one-dimensional objects viewed in transmission electron microscopy
To obtain a more complete understanding of material microstructure at the nanoscale and to gain profound insights into their properties, there is a growing need for more efficient and precise methods that can streamline the process of 3D imaging using a tr ...
EPFL2023Predicting nonlinear optical scattering with physics-driven neural networks
Demetri Psaltis, Carlo Gigli, Ahmed Ayoub
Deep neural networks trained on physical losses are emerging as promising surrogates for nonlinear numerical solvers. These tools can predict solutions to Maxwell's equations and compute gradients of output fields with respect to the material and geometric ...
AIP Publishing2023A Neural-Network-Based Convex Regularizer for Inverse Problems
Michaël Unser, Pakshal Narendra Bohra, Alexis Marie Frederic Goujon, Sebastian Jonas Neumayer, Stanislas Ducotterd
The emergence of deep-learning-based methods to solve image-reconstruction problems has enabled a significant increase in quality. Unfortunately, these new methods often lack reliability and explainability, and there is a growing interest to address these ...
2023Regularization of polynomial networks for image recognition
Volkan Cevher, Grigorios Chrysos, Bohan Wang
Deep Neural Networks (DNNs) have obtained impressive performance across tasks, however they still remain as black boxes, e.g., hard to theoretically analyze. At the same time, Polynomial Networks (PNs) have emerged as an alternative method with a promising ...
2023Artificial Neural Network Training on an Optical Processor via Direct Feedback Alignment
Florent Gérard Krzakala, Julien Marcel Daniel Emmanuel Launay
Artificial Neural Networks (ANN) are habitually trained via the back-propagation (BP) algorithm. This approach has been extremely successful: Current models like GPT-3 have O(10 11 ) parameters, are trained on O(10 11 ) words and produce awe-inspiring resu ...
IEEE2023Divergent X-ray tomography reconstruction and optimisation
In diverse fields such as medical imaging, astrophysics, geophysics, or material study, a common challenge exists: reconstructing the internal volume of an object using only physical measurements taken from its exterior or surface. This scientific approach ...
2023On double-descent in uncertainty quantification in overparametrized models
Florent Gérard Krzakala, Lenka Zdeborová, Lucas Andry Clarte, Bruno Loureiro, Bruno Loureiro
Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ra ...
PMLR Proceedings of Machine Learning Research2023Maximum likelihood bolometry for ASDEX upgrade experiments
Bolometry is an essential diagnostic for calculating the power balances and for the understanding of different physical aspects of tokamak experiments. The reconstruction method based on the Maximum Likelihood (ML) principle, developed initially for JET, h ...
Bristol2023