Recently, I submitted a paper titled “Learning Graphical State Transitions” to the ICLR conference. (Update: My paper was accepted! I will be giving an oral presentation at ICLR 2017 in Toulon, France. See here for more details.) In it, I describe a new type of neural network architecture, called a Gated Graph Transformer Neural Network, that is designed to use graphs as an internal state. I demonstrate its performance on the bAbI tasks as well as on some other tasks with complex rules. While the main technical details are provided in the paper, I figured it would be worthwhile to describe the motivation and basic ideas here.
Note: Before I get too far into this post, if you have read my paper and are interested in replicating or extending my experiments, the code for my model is available on GitHub.
Another thing that I’ve noticed is that almost all of the papers on machine learning are about successes. This is an example of an overall trend in science to focus on the positive results, since they are the most interesting. But it can also be very useful to discuss the negative results as well. Learning what doesn’t work is in some ways just as important as learning what does, and can save others from repeating the same mistakes. During my development of the GGT-NN, I had multiple iterations of the model, which all failed to learn anything interesting. The version of the model that worked was thus a product of an extended cycle of trial and error. In this post I will try to describe the failed models as well, and give my speculative theories for why they may not have been as successful.