Search Results for author:
Found 3 papers, 0 papers with code
Deep Learning and Geometric Deep Learning: an introduction for mathematicians and physicists
In this expository paper we want to give a brief introduction, with few key references for further reading, to the inner functioning of the new and successfull algorithms of Deep Learning and Geometric Deep Learning with a focus on Graph Neural Networks.
A precortical module for robust CNNs to light variations
We present a simple mathematical model for the mammalian low visual pathway, taking into account its key elements: retina, lateral geniculate nucleus (LGN), primary visual cortex (V1).
A geometric interpretation of stochastic gradient descent using diffusion metrics
Stochastic gradient descent (SGD) is a key ingredient in the training of deep neural networks and yet its geometrical significance appears elusive.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
