What are the inputs to the NN? The whole grid, or just nearby cells? What happens if two NNs overlap on the same grid? (Gonna go read the paper).
The input to the NN is just the 3x3 neighborhood around a cell. We can overlap two NNs on the same grid (through interpolation). Checkout https://meshnca.github.io to see the effect. When the brush is in graft mode, it basically allows you to paint some regions that will follow a different NN rule.
Is each image a NN or is it one NN for all the images?
The input to the NN is just the 3x3 neighborhood around a cell. We can overlap two NNs on the same grid (through interpolation). Checkout https://meshnca.github.io to see the effect. When the brush is in graft mode, it basically allows you to paint some regions that will follow a different NN rule.
Is each image a NN or is it one NN for all the images?
One NN per pattern/image (instance based training).
If it says [flagged], a human pressed the "flag" button. Most likely because OP is commenting too much.
I don't believe that's the case. HN is running some kind of automated slop detector that classifies any suspected LLM output as "slop" even if it's entirely in-context for the conversation (e.g., a conversation about LLM output.) The comment gets autoflagged the moment it's posted. This has been the case for a while now, I've run afoul of it myself. It sucks, and I can only imagine they implemented it because they fe…
Comments that get autokilled are just [dead], not [flagged] [dead]. If you cannot vouch comments back to life, it's also possible that your vouching privileges were taken away because the mods disagreed with your vouching decisions. When you see something really great that is [dead] and vouching doesn't help, you can try emailing the mods and mention that vouching didn't work. If it's actually great, maybe they'll let you vouch again. (Happened to me.)
For the unfamiliar, could someone explain what I'm looking at? The abstract was a little too concrete (heh) for me to follow.
If you're familiar with CAs (e.g. Conway's Game of Life), you can think of a NeuralCA as a CA where the update rule is given by a neural network. Here we optimize the neural net weights so that it behaves a certain way (e.g. grow a lizard from a single seed).
Wow. That's fascinating. Thanks for that explanation. So these images come to be consequentially from initial state and weights...
Fascinating. A glimpse into the mathematics of life and living tissue. A toy example like this might herald self-healing machines, in the same way that the first perceptrons heralded Fable and nude deepfakes.