Reverse Engineering a Neural Network’s Clever Solution to Binary Addition

There’s a ton of attention lately on massive neural networks with billions of parameters, and rightly so. By combining huge parameter counts with powerful architectures like transformers and diffusion, neural networks are capable of accomplishing astounding feats.

However, even small networks can be surprisingly effective - especially when they’re specifically designed for a specialized use-case. As part of some previous work I did, I was training small (<1000 parameter) networks to generate sequence-to-sequence mappings and perform other simple logic tasks. I wanted the models to be as small and simple as possible with the goal of building little interactive visualizations of their internal states.

Ich liebe diese Artikel und sie sind viel zu selten. Ein überschaubares, aber interessantes Problem mit neuronalen Netzen gelernt. Statt so viel wie möglich compute auf das Problem zu werfen auf’s Wesentliche reduziert und echte Erkenntnisse geliefert.

Die Rolle der Aktivierungsfunktion in dem Fall finde ich besonders Interessant, weil es mehr als nur eine nicht-lineare Funktion ist.