Artificial intelligence, especially deep learning & neural networks for image processing and classfication, are related to statistics and physics e.g. as decribed in below papers.
Statistics and AI
Physics and AI
A relation of deep learning and physics (renormalization group) is elaborated in:
Also category theory is used to describe neural systems and single aspects like e.g. back propagation.
Category theory and AI
Question. Are there interesting formulations and generalizations of (convolutional) neural networks (CNNs) / deep learning (or to machine learning in general) concepts in an algebraic context?
For example I read something about Clifford Neural Networks based on Clifford Algebras
mentioning Clifford algebra neural computing. However as far as I understand, this is more a generalization of neuron's in- and output values rather than an algebraic formulation of neural network related concepts?
In a similar direction(?) goes e.g.
Remark. I am aware that this question could also be posted at stack overflow or some other stack exchange site. However my idea is that it could be better to ask it on the mathematics site, since it might rather be mathematicians that have some knowledge about such connections than e.g. computer scientists themselves.