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Training fully connected networks with resistive memories: impact of device failures

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Publications associées à Training fully connected networks with resistive memories: impact of device failures | 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

Training fully connected networks with resistive memories: impact of device failures

Graph Chatbot

Chattez avec Graph Search

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

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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

Random matrix methods for high-dimensional machine learning models

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

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Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

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