Publications related to Backpropagation-Friendly Eigendecomposition

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

EPFL2024

Data and scripts for the RaFSIP scheme

Athanasios Nenes, Paraskevi Georgakaki

This repository contains microphysics routines, scripts, and processed data from the Weather Research and Forecasting (WRF) model simulations presented in the paper "RaFSIP: Parameterizing ice multiplication in models using a machine learning approach", by ...

Zenodo2024

Hybrid Simulator for Capturing Dynamics of Synthetic Populations

Michel Bierlaire, Marija Kukic

This paper presents a novel hybrid framework for generating and updating a synthetic population. We call it hybrid because it combines model-based and data-driven approaches. Existing generators produce a snapshot of synthetic data that becomes outdated ov ...

IEEE2024

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

Data-driven statistical optimization of a groundwater monitoring network

Andrea Rinaldo, Gianluca Botter

We propose a comparative study of three different methods aimed at optimizing existing groundwater monitoring networks. Monitoring piezometric heads in subsurface porous formations is crucial at regional scales to properly characterize the relevant subsurf ...

Elsevier2024

Robust mass lumping and outlier removal strategies in isogeometric analysis

Annalisa Buffa, Espen Sande, Yannis Dirk Voet

Mass lumping techniques are commonly employed in explicit time integration schemes for problems in structural dynamics and both avoid solving costly linear systems with the consistent mass matrix and increase the critical time step. In isogeometric analysi ...

2024

Probabilistic and Deterministic Wellposedness for Low Regularity Dispersive Equations

Katie Sabrina Catherine Rosie Marsden

In this thesis we will present two results on global existence for nonlinear dispersive equations with data at or below the scaling regularity. In chapter 1 we take a probabilistic perspective to study the energy-critical nonlinear Schrödinger equation in ...

EPFL2024

Orchestrating chromosome conformation capture analysis with Bioconductor

Genome-wide chromatin conformation capture assays provide formidable insights into the spatial organization of genomes. However, due to the complexity of the data structure, their integration in multi-omics workflows remains challenging. We present data st ...

2024

Dataset for "Complexity of crack front geometry enhances toughness of brittle solids"

John Martin Kolinski, Chenzhuo Li, Xinyue Wei

This data package supports the publication 'Complexity of crack front geometry enhances toughness of brittle solids' by Xinyue Wei, Chenzhuo Li, Cían McCarthy, and John M. Kolinski Nature physics (2024) - https://doi.org/10.1038/s41567-024-02435-x DOI: 1 ...

Zenodo2024

Pore size estimation in axon-mimicking microfibers with diffusion-relaxation MRI

Jean-Philippe Thiran, Erick Jorge Canales Rodriguez, Muhamed Barakovic, Marco Pizzolato, Tim Bjørn Dyrby

Purpose: This study aims to evaluate two distinct approaches for fiber radius estimation using diffusion-relaxation MRI data acquired in biomimetic microfiber phantoms that mimic hollow axons. The methods considered are the spherical mean power-law approac ...

Hoboken2024

Backpropagation-Friendly Eigendecomposition

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Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Data and scripts for the RaFSIP scheme

Hybrid Simulator for Capturing Dynamics of Synthetic Populations

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Data-driven statistical optimization of a groundwater monitoring network

Robust mass lumping and outlier removal strategies in isogeometric analysis

Probabilistic and Deterministic Wellposedness for Low Regularity Dispersive Equations

Orchestrating chromosome conformation capture analysis with Bioconductor

Dataset for "Complexity of crack front geometry enhances toughness of brittle solids"

Pore size estimation in axon-mimicking microfibers with diffusion-relaxation MRI

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Probabilistic and Deterministic Wellposedness for Low Regularity Dispersive Equations

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Orchestrating chromosome conformation capture analysis with Bioconductor

Data-driven statistical optimization of a groundwater monitoring network

Pore size estimation in axon-mimicking microfibers with diffusion-relaxation MRI

Robust mass lumping and outlier removal strategies in isogeometric analysis

Hybrid Simulator for Capturing Dynamics of Synthetic Populations

Data and scripts for the RaFSIP scheme

Dataset for "Complexity of crack front geometry enhances toughness of brittle solids"