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Neural Networks in Front-End Processing and Control

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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

Neural Networks in Front-End Processing and Control

Graph Chatbot

Chattez avec Graph Search

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Artificial Neural Network Training on an Optical Processor via Direct Feedback Alignment

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Distributed neural network control with dependability guarantees: a compositional port-Hamiltonian approach

Improving the Training of Compact Neural Networks for Visual Recognition

Autonomous Detection and Deterrence of Pigeons on Buildings by Drones

Advances In Morphological Neural Networks: Training, Pruning And Enforcing Shape Constraints

Biologically plausible unsupervised learning in shallow and deep neural networks

Lupulus: A Flexible Hardware Accelerator For Neural Networks

Autonomous Detection and Deterrence of Pigeons on Buildings by Drones

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Artificial Neural Network Training on an Optical Processor via Direct Feedback Alignment

Improving the Training of Compact Neural Networks for Visual Recognition

Lupulus: A Flexible Hardware Accelerator For Neural Networks

Advances In Morphological Neural Networks: Training, Pruning And Enforcing Shape Constraints

Biologically plausible unsupervised learning in shallow and deep neural networks

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Distributed neural network control with dependability guarantees: a compositional port-Hamiltonian approach