I have recently written a paper on understanding machine learning via the lens of Hopf algebra https://arxiv.org/abs/2302.01834 https://arxiv.org/abs/2302.01834

I have recently written a paper on understanding machine learning via the lens of Hopf algebra https://arxiv.org/abs/2302.01834 https://arxiv.org/abs/2302.01834. Hopf algebras (which are really just tensors with recurrence relations built in) subsume convnets, transformers and diffusion model and also provide a theoretically better autodiff that operates within single layers as opposed to across entire graphs. Furthermore, there is a correspondence between Hopf algebra and linear logic and Hopf algebras are related to zonotopes, which are polyhedra that have been used in verified numerical computation. I'm strongly convinced the LL connection can provide proofs over zonotopes which paves the way towards interpretable AI and will be central for alignment. I'm working on a next gen Hopf algebra based machine learning framework. Join my discord if you want to discuss this further https://discord.cofunctional.ai https://discord.cofunctional.ai.