Shared by Jacob Lauritzen (@jacsebl), CTO at Legora.
They got Fable to directly hand-write the weights of a 100k-parameter neural network to classify real vs. fake legal citations, outperforming traditional gradient descent training while staying super sparse.
This is just a huge if/then/else chain setup as model weights.
Seems like it made if/then/else decisions until it was optimized to the test. I bet there's a massive over-fitting.
Shared by Jacob Lauritzen (@jacsebl), CTO at Legora.
They got Fable to directly hand-write the weights of a 100k-parameter neural network to classify real vs. fake legal citations, outperforming traditional gradient descent training while staying super sparse.