So, hand-coded weights can do it with 36 params and 311 for trained weights - did anyone try the former architecture, but starting with random weights and learning?
> In short: if you can swap in a different set of weights and use the exact same inference code for a different task, your setup is legitimate. If the inference code is inseparable from the algorithm, it's not.
I wonder why they don't just write the code themselves, so by design the focus can be on the model.
Not sure how much this fits into the rules but I saw on twitter someone claimed 28 params : https://gist.github.com/SeuperHakkerJa/da3050739bea97aabd86e...
So, hand-coded weights can do it with 36 params and 311 for trained weights - did anyone try the former architecture, but starting with random weights and learning?
> In short: if you can swap in a different set of weights and use the exact same inference code for a different task, your setup is legitimate. If the inference code is inseparable from the algorithm, it's not.
I wonder why they don't just write the code themselves, so by design the focus can be on the model.
You can do that in a single matmul of course.
So can you take an arbitrary transformer and somehow turn it into a compact set of low-power fast gates by some algorithm?
I think you're misunderstanding the joke.