Marco Lorenzi | DeepAI

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∙ 09/13/2023

Tackling the dimensions in imaging genetics with CLUB-PLS

A major challenge in imaging genetics and similar fields is to link high...

0 Andre Altmann, et al. ∙

∙ 06/05/2023

On Tail Decay Rate Estimation of Loss Function Distributions

The study of loss function distributions is critical to characterize a m...

0 Etrit Haxholli, et al. ∙

∙ 06/05/2023

Enhanced Distribution Modelling via Augmented Architectures For Neural ODE Flows

While the neural ODE formulation of normalizing flows such as in FFJORD ...

0 Etrit Haxholli, et al. ∙

∙ 06/05/2023

Faster Training of Diffusion Models and Improved Density Estimation via Parallel Score Matching

In Diffusion Probabilistic Models (DPMs), the task of modeling the score...

0 Etrit Haxholli, et al. ∙

∙ 04/24/2023

Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications

The real-world implementation of federated learning is complex and requi...

0 Francesco Cremonesi, et al. ∙

∙ 04/17/2023

Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models

Federated learning allows for the training of machine learning models on...

0 Irene Balelli, et al. ∙

∙ 11/21/2022

Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization

The aim of Machine Unlearning (MU) is to provide theoretical guarantees ...

0 Yann Fraboni, et al. ∙

∙ 06/21/2022

A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates

We propose a novel framework to study asynchronous federated learning op...

0 Yann Fraboni, et al. ∙

∙ 05/17/2022

Privacy Preserving Image Registration

Image registration is a key task in medical imaging applications, allowi...

0 Riccardo Taiello, et al. ∙

∙ 04/15/2022

A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations

We propose a novel federated learning paradigm to model data variability...

0 Irene Balelli, et al. ∙

∙ 07/26/2021

On The Impact of Client Sampling on Federated Learning Convergence

While clients' sampling is a central operation of current state-of-the-a...

0 Yann Fraboni, et al. ∙

∙ 05/12/2021

Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning

This work addresses the problem of optimizing communications between ser...

0 Yann Fraboni, et al. ∙

∙ 04/30/2021

Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas

Joint registration of a stack of 2D histological sections to recover 3D ...

13 Adrià Casamitjana, et al. ∙

∙ 01/22/2021

Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentation

The use of deep learning techniques for 3D brain vessel image segmentati...

23 Vien Ngoc Dang, et al. ∙

∙ 10/02/2020

Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets

The use of mechanistic models in clinical studies is limited by the lack...

0 Jaume Banus, et al. ∙

∙ 06/21/2020

Free-rider Attacks on Model Aggregation in Federated Learning

Free-rider attacks on federated learning consist in dissimulating partic...

0 Yann Fraboni, et al. ∙

∙ 05/23/2019

A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments

In this study we propose a deformation-based framework to jointly model ...

0 Raphaël Sivera, et al. ∙

∙ 02/28/2019

Monotonic Gaussian Process for Spatio-Temporal Trajectory Separation in Brain Imaging Data

We introduce a probabilistic generative model for disentangling spatio-t...

0 Clement Abi Nader, et al. ∙

∙ 01/11/2019

DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders

Here we present DIVE: Data-driven Inference of Vertexwise Evolution. DIV...

0 Razvan V. Marinescu, et al. ∙

∙ 01/11/2019

Disease Knowledge Transfer across Neurodegenerative Diseases

We introduce Disease Knowledge Transfer (DKT), a novel technique for tra...

0 Razvan V. Marinescu, et al. ∙

∙ 10/19/2018

Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data

At this moment, databanks worldwide contain brain images of previously u...

0 Santiago Silva, et al. ∙

∙ 08/20/2018

Alzheimer's Disease Modelling and Staging through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes

Alzheimer's disease (AD) is characterized by complex and largely unknown...

0 Clement Abi Nader, et al. ∙

∙ 08/10/2018

Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease

The joint analysis of biomedical data in Alzheimer's Disease (AD) is imp...

0 Luigi Antelmi, et al. ∙

∙ 02/15/2018

Constraining the Dynamics of Deep Probabilistic Models

We introduce a novel generative formulation of deep probabilistic models...

0 Marco Lorenzi, et al. ∙

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