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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

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

Optimization framework for calibration of constitutive models enhanced by neural networks

Graph Chatbot

Chattez avec Graph Search

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)

Autoencoders reloaded

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

Financial Risk Management with Machine Learning

The spectral bias of polynomial neural networks

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

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

Predicting in Uncertain Environments: Methods for Robust Machine Learning

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