Publications related to A synchrotron X-ray imaging strategy to map large animal brains

Unraveling behavior and cortical signals to guide the development of soft neuroprostheses for auditory restoration and spreading depolarization

Neuroprostheses have been used clinically for decades, to help restore or preserve brain functions, when pharmaceutical treatments are inefficient. Although great progress in the field has been made over the years to interface with the nervous system, surf ...

EPFL2024

Machine Learning for Modeling Stock Returns

Teng Andrea Xu

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, ...

EPFL2024

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

Mattia Atzeni

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 ...

EPFL2024

Optimizing Dynamic Aperture Studies with Active Learning

Tatiana Pieloni, Ekaterina Krymova, Davide Di Croce, Massimo Giovannozzi

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

2024

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Martin Jaggi, Vinitra Swamy, Jibril Albachir Frej, Julian Thomas Blackwell

Interpretability for neural networks is a trade-off between three key requirements: 1) faithfulness of the explanation (i.e., how perfectly it explains the prediction), 2) understandability of the explanation by humans, and 3) model performance. Most exist ...

2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

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

2

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

EPFL2024

Intraday solar irradiance forecasting using public cameras

Demetri Psaltis, Mario Paolone, Christophe Moser, Luisa Lambertini

With the significant increase in photovoltaic (PV) electricity generation, more attention has been given to PV power forecasting. Indeed, accurate forecasting allows power grid operators to better schedule and dispatch their assets, such as energy storage ...

Pergamon-Elsevier Science Ltd2024

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

Marco Picasso, Alexandre Caboussat, Maude Girardin

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 ...

Lausanne2024

Random matrix methods for high-dimensional machine learning models

Antoine Philippe Michel Bodin

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 ...

EPFL2024

Transient brain activity dynamics discriminate levels of consciousness during anesthesia

Dimitri Nestor Alice Van De Ville, Ayberk Ozkirli, Elvira Pirondini

The awake mammalian brain is functionally organized in terms of large-scale distributed networks that are constantly interacting. Loss of consciousness might disrupt this temporal organization leaving patients unresponsive. We hypothesize that characterizi ...

Nature Portfolio2024

A synchrotron X-ray imaging strategy to map large animal brains

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Unraveling behavior and cortical signals to guide the development of soft neuroprostheses for auditory restoration and spreading depolarization

Machine Learning for Modeling Stock Returns

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

Optimizing Dynamic Aperture Studies with Active Learning

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Intraday solar irradiance forecasting using public cameras

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

Random matrix methods for high-dimensional machine learning models

Transient brain activity dynamics discriminate levels of consciousness during anesthesia

Unraveling behavior and cortical signals to guide the development of soft neuroprostheses for auditory restoration and spreading depolarization

Machine Learning for Modeling Stock Returns

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

Optimizing Dynamic Aperture Studies with Active Learning

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Intraday solar irradiance forecasting using public cameras

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

Random matrix methods for high-dimensional machine learning models

Transient brain activity dynamics discriminate levels of consciousness during anesthesia