Publications related to Optimization framework for calibration of constitutive models enhanced by neural networks

Deep Learning Theory Through the Lens of Diagonal Linear Networks

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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EPFL2024

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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

Privacy-preserving federated neural network training and inference

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Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing, transferring, and centralizing the data from silos, however, is difficult due to current privacy regulations (e.g., H ...

EPFL2023

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Hervé Bourlard, Selen Hande Kabil

In Bourlard and Kamp (Biol Cybern 59(4):291-294, 1998), it was theoretically proven that autoencoders (AE) with single hidden layer (previously called "auto-associative multilayer perceptrons") were, in the best case, implementing singular value decomposit ...

SPRINGER2022

Financial Risk Management with Machine Learning

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This thesis consists of three applications of machine learning techniques to risk management. The first chapter proposes a deep learning approach to estimate physical forward default intensities of companies. Default probabilities are computed using artifi ...

EPFL2022

Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study

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Neural tangent kernel (NTK) is a powerful tool to analyze training dynamics of neural networks and their generalization bounds. The study on NTK has been devoted to typical neural network architectures, but it is incomplete for neural networks with Hadamar ...

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Predicting in Uncertain Environments: Methods for Robust Machine Learning

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One of the main goal of Artificial Intelligence is to develop models capable of providing valuable predictions in real-world environments. In particular, Machine Learning (ML) seeks to design such models by learning from examples coming from this same envi ...

EPFL2022

The spectral bias of polynomial neural networks

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Polynomial neural networks (PNNs) have been recently shown to be particularly effective at image generation and face recognition, where high-frequency information is critical. Previous studies have revealed that neural networks demonstrate a spectral bias ...

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Discovery of slow variables in a class of multiscale stochastic systems via neural networks

Jan Sickmann Hesthaven, Przemyslaw Zielinski

Finding a reduction of complex, high-dimensional dynamics to its essential, low-dimensional "heart" remains a challenging yet necessary prerequisite for designing efficient numerical approaches. Machine learning methods have the potential to provide a gene ...

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Neural Tangent Kernel: Convergence and Generalization in Neural Networks (Invited Paper)

Clément Hongler, Franck Raymond Gabriel, Arthur Jacot

The Neural Tangent Kernel is a new way to understand the gradient descent in deep neural networks, connecting them with kernel methods. In this talk, I'll introduce this formalism and give a number of results on the Neural Tangent Kernel and explain how th ...

ASSOC COMPUTING MACHINERY2021

Optimization framework for calibration of constitutive models enhanced by neural networks

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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

Privacy-preserving federated neural network training and inference

Autoencoders reloaded

Financial Risk Management with Machine Learning

Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study

Predicting in Uncertain Environments: Methods for Robust Machine Learning

The spectral bias of polynomial neural networks

Discovery of slow variables in a class of multiscale stochastic systems via neural networks

Neural Tangent Kernel: Convergence and Generalization in Neural Networks (Invited Paper)

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

The spectral bias of polynomial neural networks

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Autoencoders reloaded

Privacy-preserving federated neural network training and inference

Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study

Discovery of slow variables in a class of multiscale stochastic systems via neural networks

Neural Tangent Kernel: Convergence and Generalization in Neural Networks (Invited Paper)

Financial Risk Management with Machine Learning