Publications related to Full characterization of partially measured systems with neural networks

Related publications (24)

Capturing behavioral and neural variability in multi-area spiking neuronal network models

How does the brain generate behavioral responses from sensory stimuli? This sensor-to-motor transformation is a fundamental question in systems neuroscience. While recent experiments have highlighted the complexity of neural representations across the cere ...

EPFL2025

Parametrizations of all stable closed-loop responses: From theory to neural network control design

Giancarlo Ferrari Trecate, Luca Furieri, Clara Lucía Galimberti

The complexity of modern control systems necessitates architectures that achieve high performance while ensuring robust stability, particularly for nonlinear systems. In this work, we tackle the challenge of designing output-feedback controllers to boost t ...

2025

Aggregating Spatial and Photometric Context for Photometric Stereo

David Honzátko

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

EPFL2024

Accelerating Transfer Learning with Near-Data Computation on Cloud Object Stores

Rachid Guerraoui, Arsany Hany Abdelmessih Guirguis, Diana Andreea Petrescu

Storage disaggregation underlies today's cloud and is naturally complemented by pushing down some computation to storage, thus mitigating the potential network bottleneck between the storage and compute tiers. We show how ML training benefits from storage ...

Association for Computing Machinery, Inc2024

Drone‐Based Solar Cell Inspection With Autonomous Deep Learning

Mirko Kovac

Regular inspection and maintenance are crucial for ensuring the optimal performance of solar panels. However, conventional manual methods can be laborious, time consuming, and expensive, especially for large and inaccessible installations. Aerial inspectio ...

Wiley2024

Solving Non-linear Kolmogorov Equations in Large Dimensions by Using Deep Learning: A Numerical Comparison of Discretization Schemes

Nicolas Macris

Non-linear partial differential Kolmogorov equations are successfully used to describe a wide range of time dependent phenomena, in natural sciences, engineering or even finance. For example, in physical systems, the Allen-Cahn equation describes pattern f ...

SPRINGER/PLENUM PUBLISHERS2023

2D Nanosystems: Applications of 2D Semiconductors for In-Memory Computing

Guilherme Migliato Marega

Machine learning and data processing algorithms have been thriving in finding ways of processing and classifying information by exploiting the hidden trends of large datasets. Although these emerging computational methods have become successful in today's ...

EPFL2023

Euclid: Identification of asteroid streaks in simulated images using deep learning

Frédéric Courbin, Georges Meylan

The material composition of asteroids is an essential piece of knowledge in the quest to understand the formation and evolution of the Solar System. Visual to near-infrared spectra or multiband photometry is required to constrain the material composition o ...

Edp Sciences S A2023

Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs

Bahar Radmehr, Tanja Christina Käser Jacober, Mirko Marras, Vinitra Swamy

Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainability of their decisions, especially relevant in human-centric fields. We im ...

2022

Status of the K-EUSO Orbital Detector of Ultra-High Energy Cosmic Rays

Andrii Neronov

K-EUSO (KLYPVE-EUSO) is a planned orbital mission aimed at studying ultra-high energy cosmic rays (UHECRs) by detecting fluorescence and Cherenkov light emitted by extensive air showers in the nocturnal atmosphere of Earth in the ultraviolet (UV) range. Th ...

MDPI2022

Adjusting the Ground Truth Annotations for Connectivity-Based Learning to Delineate

Pascal Fua, Mateusz Kozinski, Doruk Oner, Leonardo Citraro

Deep learning-based approaches to delineating 3D structure depend on accurate annotations to train the networks. Yet in practice, people, no matter how conscientious, have trouble precisely delineating in 3D and on a large scale, in part because the data i ...

Institute of Electrical and Electronics Engineers2022

Deep learning architectures for estimating breathing signal and respiratory parameters from speech recordings

Mathew Magimai Doss, Zohreh Mostaani

Respiration is an essential and primary mechanism for speech production. We first inhale and then produce speech while exhaling. When we run out of breath, we stop speaking and inhale. Though this process is involuntary, speech production involves a system ...

PERGAMON-ELSEVIER SCIENCE LTD2021

Geometric compression of invariant manifolds in neural networks

Matthieu Wyart, Mario Geiger, Leonardo Petrini, Jonas Paccolat

We study how neural networks compress uninformative input space in models where data lie in d dimensions, but the labels of which only vary within a linear manifold of dimension d(parallel to) < d. We show that for a one-hidden-layer network initialized wi ...

IOP PUBLISHING LTD2021

Reusability report: Predicting spatiotemporal nonlinear dynamics in multimode fibre optics with a recurrent neural network

Demetri Psaltis, Christophe Moser, Niyazi Ulas Dinç, Ugur Tegin

SPRINGERNATURE2021

Far-Field Subwavelength Acoustic Imaging by Deep Learning

Romain Christophe Rémy Fleury, Bakhtiyar Orazbayev

Seeing and recognizing an object whose size is much smaller than the illumination wavelength is a challenging task for an observer placed in the far field, due to the diffraction limit. Recent advances in near- and far-field microscopy have offered several ...

American Physical Society2020

Concept Discovery for The Interpretation of Landscape Scenicness

Devis Tuia

In this paper, we study how to extract visual concepts to understand landscape scenicness. Using visual feature representations from a Convolutional Neural Network (CNN), we learn a number of Concept Activation Vectors (CAV) aligned with semantic concepts ...

2020

Systematic model reduction captures the dynamics of extrinsic noise in biochemical subnetworks

Barbara Bravi

We consider the general problem of describing the dynamics of subnetworks of larger biochemical reaction networks, e.g., protein interaction networks involving complex formation and dissociation reactions. We propose the use of model reduction strategies t ...

AMER INST PHYSICS2020

Comparing dynamics: deep neural networks versus glassy systems

Matthieu Wyart, Mario Geiger, Stefano Spigler, Levent Dogus Sagun

We analyze numerically the training dynamics of deep neural networks (DNN) by using methods developed in statistical physics of glassy systems. The two main issues we address are (1) the complexity of the loss landscape and of the dynamics within it, and ( ...

IOP PUBLISHING LTD2019

Unsupervised Geometry-Aware Representation Learning for 3D Human Pose Estimation

Pascal Fua, Mathieu Salzmann, Helge Jochen Rhodin

Modern 3D human pose estimation techniques rely on deep networks, which require large amounts of training data. While weakly-supervised methods require less supervision, by utilizing 2D poses or multi-view imagery without annotations, they still need a suf ...

2018

Deep Residual Network for Joint Demosaicing and Super- Resolution

Sabine Süsstrunk, Radhakrishna Achanta, Ruofan Zhou

The two classic image restoration tasks, demosaicing and super-resolution, have traditionally always been studied indepen- dently. That is sub-optimal as sequential processing, demosaic- ing and then super-resolution, may lead to amplification of ar- tifac ...

Society for Imaging Science and Technology ( IS&T )2018