Comment by adamnemecek

Yes, all machine learning can be interpreted in terms of approximating the partition function.

This is obvious when one considers the connections between Transformers, RNNs, Hopfield networks and the Ising model, a model from statistical mechanics which is solved by calculating the partition function.

This interpretation provides us with some very powerful tools that are commonplace in math and physics but which are not talked about in CS & ML.

I'm working on a startup http://traceoid.ai which takes this exact view. Our approach enables faster training and inference, interpretability and also scalable energy-based models, the Holy Grail of machine learning.

Join the discord https://discord.com/invite/mr9TAhpyBW or follow me on twitter https://twitter.com/adamnemecek1