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Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element

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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

Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element

Graph Chatbot

Chattez avec Graph Search

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Random matrix methods for high-dimensional machine learning models

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

Explainable Face Verification via Feature-Guided Gradient Backpropagation

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

Topics in statistical physics of high-dimensional machine learning

Robust NAS under adversarial training: benchmark, theory, and beyond

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

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

Explainable Face Verification via Feature-Guided Gradient Backpropagation

Random matrix methods for high-dimensional machine learning models

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

Topics in statistical physics of high-dimensional machine learning

Robust NAS under adversarial training: benchmark, theory, and beyond