Category Theory ∩ Machine Learning
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Category Theory ∩ Machine Learning
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Re: Category Theory ∩ Machine Learning
#2Is this simply a consequence of exponential growth in CS publications driven by machine learning or is there something really going on here?
Re: Category Theory ∩ Machine Learning
#3I have recently written a paper on understanding machine learning via the lens of Hopf algebra 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 cyclical 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 XAI.
I know this sounds too good to be true but Persi Diaconis has also written a paper that shows how useful Hopf algebras are in the context of Markov chains https://arxiv.org/abs/1206.3620
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.
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My account is currently rate limited so I will use this comment to respond to comments below.
red_trumped: What about Hopf algebras do I not understand?
gaze: Haha, it's been a while since I have commented about QC. What do I not understand about it? And what comment are you referring to?
Re: Category Theory ∩ Machine Learning
#4Is this simply a consequence of exponential growth in CS publications driven by machine learning or is there something really going on here?
The field needs better foundations. CT is pretty good.
Re: Category Theory ∩ Machine Learning
#5Is this simply a consequence of exponential growth in CS publications driven by machine learning or is there something really going on here?
The field needs better foundations. CT is pretty good.
No. It won't make a significant (if any at all) difference to effectiveness. Rewriting Pytorch in Haskell won't magically get you AGI.
Re: Category Theory ∩ Machine Learning
#6I have recently written a paper on understanding machine learning via the lens of Hopf algebra 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…
could you advertise your research a bit less often, please? i see your post like literally almost every other day here
Re: Category Theory ∩ Machine Learning
#7Re: Category Theory ∩ Machine Learning
#8Earlier quoted context omitted.
The field needs better foundations. CT is pretty good.
No. It won't make a significant (if any at all) difference to effectiveness. Rewriting Pytorch in Haskell won't magically get you AGI.
It's not about rewriting things in Haskell but about using CT to reason about architectures.
Re: Category Theory ∩ Machine Learning
#9Is this simply a consequence of exponential growth in CS publications driven by machine learning or is there something really going on here?
The field needs better foundations. CT is pretty good.
Why? How?
The OP GitHub site doesn't promote any material that introduces the concepts at all. The "survey" paper at the top is nigh-impenetrable. I'm sure the category theorists are having fun modelling machine learning, but it doesn't show how machine learning benefits from the category theory.
Re: Category Theory ∩ Machine Learning
#10I have recently written a paper on understanding machine learning via the lens of Hopf algebra 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…
could you advertise your research a bit less often, please? i see your post like literally almost every other day here
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