models for images; talking about stuff in progress

As my loyal reader knows, I have been working on new machine-learning methods for imaging, based on scalars, vectors, and tensors, and group-theoretic invariants. Today I had a long conversation with Kaze Wong (Flatiron) about these things, with ideas about implementation and model structure. With Soledad Villar (JHU) I have been thinking about convolutional layers, and nonlinearities, but Wong wanted to talk about full-network architecture. It's hard! So we discussed, and realized that we need to do some serious work if we are going to find a way to implement something general and useful and computationally tractable.

Before that, in Astronomical Data Group meeting, I got tons of great feedback about my project with Andy Casey (Monash) to combine shifted spectra into a rest-frame average spectrum. I described the project in the words I like to use, and the audience heard something totally different! So I know what I have to be emphasizing in the writing. I can't say enough how important it is to talk to people about your science in progress!