How would CAs vary the amount of compute? Don't you have to compute everything state-wise every iteration?
Right now my understanding is that in neural cellular automata people replace the update rule with a DNN. And this DNN is trained on small inputs. Basically a cell's neighborhood input vs a "pixel" vs token level input... a cellular neighborhood here is basically patches which aligns with DNNs anyway.
How would CAs vary the amount of compute? Don't you have to compute everything state-wise every iteration?
Right now my understanding is that in neural cellular automata people replace the update rule with a DNN. And this DNN is trained on small inputs. Basically a cell's neighborhood input vs a "pixel" vs token level input... a cellular neighborhood here is basically patches which aligns with DNNs anyway.
A good example is: https://distill.pub/2020/growing-ca/
The examples remind of inpainting though in some sense.
You can apply transformers to this to get a shared memory (people have done this I believe).
Too be honest I feel like neural CAs are a trick but I am probably wrong.