Publications related to Artificial neural network

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

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

EPFL Infoscience2024

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Volkan Cevher, Grigorios Chrysos, Fanghui Liu

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

2024

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

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Romain Christophe Rémy Fleury, Janez Rus

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

2024

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

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

EPFL Infoscience2024

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

ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees

Daniel Gatica-Perez, Sina Sajadmanesh

Graph Neural Networks (GNNs) have become a popular tool for learning on graphs, but their widespread use raises privacy concerns as graph data can contain personal or sensitive information. Differentially private GNN models have been recently proposed to p ...

Assoc Computing Machinery2024

On the number of regions of piecewise linear neural networks

Michaël Unser, Alexis Marie Frederic Goujon

Many feedforward neural networks (NNs) generate continuous and piecewise-linear (CPWL) mappings. Specifically, they partition the input domain into regions on which the mapping is affine. The number of these so-called linear regions offers a natural metric ...

2024

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

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Intraday solar irradiance forecasting using public cameras

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

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

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Deep Learning Theory Through the Lens of Diagonal Linear Networks

ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees

On the number of regions of piecewise linear neural networks

Generalization of Scaled Deep ResNets in the Mean-Field Regime

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

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

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

Intraday solar irradiance forecasting using public cameras

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees

On the number of regions of piecewise linear neural networks