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Identification of NARX models using regularization networks: A consistency result

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Publications associées à Identification of NARX models using regularization networks: A consistency result | EPFL Graph Search

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

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EPFL2024

Identification of NARX models using regularization networks: A consistency result

Graph Chatbot

Chattez avec Graph Search

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

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

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

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

On the number of regions of piecewise linear neural networks

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Random matrix methods for high-dimensional machine learning models

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: Neural network model weights

Enabling Uncertainty Estimation in Iterative Neural Networks

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms