Publications associées à PM2.5 Monitoring: Use Information Abundance Measurement and Wide and Deep Learning

Publications associées (26)

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

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

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

Alexandre Schmid

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

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 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

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

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

Intelligent RF System for Ultra Low Power Spectrum Sensing with Machine Learning

Naci Pekçokgüler

The crowdedness of the RF spectrum constitutes problems in communication, however it also accommodates large amount of useful information. This information can be used to determine existing signals and operating contexts. However, spectrum condition varies ...

EPFL2024

Fashioning Creative Expertise with Generative AI: Graphical Interfaces for GAN-Based Design Space Exploration Better Support Ideation Than Text Prompts for Diffusion Models

Pierre Dillenbourg, Tanja Christina Käser Jacober, Richard Lee Davis, Thiemo Wambsganss, Kevin Gonyop Kim, Wei Jiang

This paper investigates the potential impact of deep generative models on the work of creative professionals. We argue that current generative modeling tools lack critical features that would make them useful creativity support tools, and introduce our own ...

2024

Inverse Rendering for PET Image Reconstruction

Wenzel Alban Jakob, Baptiste Alexandre Marie Philippe Claude Nicolet

Differentiable rendering (DR) is an emerging field that facilitates the calculation and propagation of gradients of 3D objects through images. In contrast to deep neural networks (DNNs), which generally lack the understanding of 3D objects that form the im ...

IEEE2024

Scalable constrained optimization

Maria-Luiza Vladarean

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

EPFL2024

SzCORE: Seizure Community Open-Source Research Evaluation framework for the validation of electroencephalography-based automated seizure detection algorithms

David Atienza Alonso, Una Pale, Alireza Amirshahi, Jonathan Dan, William Cappelletti

The need for high-quality automated seizure detection algorithms based on electroencephalography (EEG) becomes ever more pressing with the increasing use of ambulatory and long-term EEG monitoring. Heterogeneity in validation methods of these algorithms in ...

2024

σ-GPTs: A New Approach to Autoregressive Models

Evann Pierre Guy Courdier, Arnaud Pannatier

Autoregressive models, such as the GPT family, use a fixed order, usually left-to-right, to generate sequences. However, this is not a necessity. In this paper, we challenge this assumption and show that by simply adding a positional encoding for the outpu ...

Springer Nature2024

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

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

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

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

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

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

Contextual Stochastic Bilevel Optimization

Daniel Kuhn, Andreas Krause, Yifan Hu

We introduce contextual stochastic bilevel optimization (CSBO) -- a stochastic bilevel optimization framework with the lower-level problem minimizing an expectation conditioned on some contextual information and the upper-level decision variable. This fram ...

2023

Comparison of regression techniques for generating surrogate models to predict the thermodynamic behavior of biomass gasification systems

François Maréchal, Daniel Alexander Florez Orrego, Julia Granacher, Meire Ellen Gorete Ribeiro Domingos, Marie Jane Francine Jones

Biomass resources can play an important role in the energy transition, being a dynamic feedstock that can be transformed either into solid, liquid or gaseous fuels. The biomass gasification is a versatile way to convert wastes into energy. In this work, th ...

Elsevier2023