recursive autoencoders

[jmikedupont2]

We discussed using a combination of autoencoders, clustering, and Monte Carlo Tree Search (MCTS) to create an inductive, recursive hierarchy of models for analyzing instruction data. This approach can automatically learn to separate constant and variable parts within instructions and potentially classify them based on their reconstruction errors. We also explored how to leverage this learned hierarchy to analyze execution traces by incorporating IO register values. This combined analysis can provide a more comprehensive understanding of program behavior.