Publications related to Interplay Between Topology and Social Learning Over Weak Graphs

Related publications (21)

Split weight distribution learning for binary neural networks exploiting nano-memristive graphene/sumanene/graphene devices

Research in artificial intelligence (AI) and neural network (NN) technologies has been boosted by recent hardware advances, while the emergence of self-synthesized materials such as nanocarbons has attracted much attention to overcome the limitations of Si ...

IOP Publishing Ltd2025

Preface: Advancing deep learning for remote sensing time series data analysis

Devis Tuia

This special issue explores the burgeoning field of deep learning for remote sensing time series analysis. The 20 contributed papers showcase diverse applications, including land cover mapping, change detection, atmospheric and biophysical/biochemical para ...

2025

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Jan Sickmann Hesthaven, Federico Pichi

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

Academic Press Inc Elsevier Science2024

Collective relational inference for learning heterogeneous interactions

Olga Fink

Interacting systems are ubiquitous in nature and engineering, ranging from particle dynamics in physics to functionally connected brain regions. Revealing interaction laws is of fundamental importance but also particularly challenging due to underlying con ...

Nature Portfolio2024

What can be learnt with wide convolutional neural networks?

Matthieu Wyart, Alessandro Favero, Francesco Cagnetta

Understanding how convolutional neural networks (CNNs) can efficiently learn high-dimensional functions remains a fundamental challenge. A popular belief is that these models harness the local and hierarchical structure of natural data such as images. Yet, ...

2024

A gentle introduction to gradient-based optimization and variational inequalities for machine learning

Yatin Dandi

The rapid progress in machine learning in recent years has been based on a highly productive connection to gradient-based optimization. Further progress hinges in part on a shift in focus from pattern recognition to decision-making and multi-agent problems ...

2024

Fast and robust parameter estimation with uncertainty quantification for the cardiac function

Alfio Quarteroni

Background and objectives: Parameter estimation and uncertainty quantification are crucial in computa-tional cardiology, as they enable the construction of digital twins that faithfully replicate the behavior of physical patients. Many model parameters reg ...

ELSEVIER IRELAND LTD2023

Injection-Induced Aseismic Slip in Tight Fractured Rocks

Brice Tanguy Alphonse Lecampion

We investigate the problem of fluid injection at constant pressure in a 2D Discrete Fracture Network (DFN) with randomly oriented and uniformly distributed frictionally-stable fractures. We show that this problem shares similarities with the simpler scenar ...

SPRINGER WIEN2023

The Fano Noise Suppression Factor and the Gm/ID FoM

Christian Enz, Hung-Chi Han

This paper establishes the close relation that exists between the Fano noise suppression factor F and the Gm/ID FoM showing that F is proportional to the product of the thermal noise excess factor γn and the normalized Gm/ID function. Taking advantage of t ...

IEEE2023

Higher-order organization of multivariate time series

Enrico Amico, Andrea Santoro

Time series analysis has proven to be a powerful method to characterize several phenomena in biology, neuroscience and economics, and to understand some of their underlying dynamical features. Several methods have been proposed for the analysis of multivar ...

NATURE PORTFOLIO2023

Convolutional neural network classifies visual stimuli from cortical response recorded with wide-field imaging in mice

Silvestro Micera

Objective. The optic nerve is a good location for a visual neuroprosthesis. It can be targeted when a subject cannot receive a retinal prosthesis and it is less invasive than a cortical implant. The effectiveness of an electrical neuroprosthesis depends on ...

IOP Publishing Ltd2023

Polynomial-time universality and limitations of deep learning

Emmanuel Abbé

The goal of this paper is to characterize function distributions that general neural networks trained by descent algorithms (GD/SGD), can or cannot learn in polytime. The results are: (1) The paradigm of general neural networks trained by SGD is poly-time ...

WILEY2023

Engineered substrates incapable of induction of chondrogenic differentiation compared to the chondrocyte imprinted substrates

Philippe Renaud, Leila Montazeri

It is well established that surface topography can affect cell functions. However, finding a reproducible and reliable method for regulating stem cell behavior is still under investigation. It has been shown that cell imprinted substrates contain micro- an ...

IOP Publishing Ltd2023

Training Efficient Controllers via Analytic Policy Gradient

Dario Floreano, Davide Scaramuzza, Valentin Wüest

Control design for robotic systems is complex and often requires solving an optimization to follow a trajectory accurately. Online optimization approaches like Model Predictive Control (MPC) have been shown to achieve great tracking performance, but requir ...

New York2023

Decentralized Gan Training Through Diffusion Learning

Ali H. Sayed, Noé Stauffer

Most available studies on distributed GAN architectures focus on implementations with a fusion center. In this work, we propose a fully decentralized scheme by employing a diffusion strategy to train a network of GANs. We interpret the training procedure a ...

IEEE2022

Adversarial training with informed data selection

Pascal Frossard, Javier Alejandro Maroto Morales

With the increasing amount of available data and advances in computing capabilities, deep neural networks (DNNs) have been successfully employed to solve challenging tasks in various areas, including healthcare, climate, and finance. Nevertheless, state-of ...

IEEE2022

Sonomyographic Prosthetic Interacion: Online Simultaneous and Proportional Control of Wrist and Hand Motions Using Semisupervised Learning

Xingchen Yang

Human hands play a very important role in daily object manipulation. Current prosthetic hands are capable of mimicking most functions of the human hand, but how to interact with prosthetic hands based on human intentions remains an open problem. In this ar ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Dynamic SCL Decoder With Path-Flipping for 5G Polar Codes

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Yifei Shen

Since polar were ratified as part of the 5G standard, low-complexity polar decoders with close-to-optimum error-rate performance have received significant attention. Compared to successive cancellation (SC) decoding, both SC list and SC flip decoding can i ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Micromechanically inspired investigation of cemented granular materials: part I-from X-ray micro tomography to measurable model variables

Edward Carlo Giorgio Andò

Cemented granular materials are abundant in nature and are often artificially produced. Their macroscopic behaviour is driven by small-scale material processes, which are generally classified as: grain breakage, cement damage and fragment rearrangement. Th ...

SPRINGER HEIDELBERG2022

Adhesive wear with a coarse-grained discrete element model

Jean-François Molinari, Son-Jonathan Pham-Ba

The use of molecular dynamics (MD) simulations has led to promising results to unravel the atomistic origins of adhesive wear, and in particular for the onset of wear at nanoscale surface asperities. However, MD simulations come with a high computational c ...

ELSEVIER SCIENCE SA2022