Maayane Soumagnac (UCL) visited for a few hours to discuss her projects on classification and inference in the Dark Energy Survey. She is using artificial neural networks, but wants to compete them or compare them with Bayesian methods that involve modeling the data probabilistically. I told her about what Fadely, Willman, and I are doing and perhaps she will start doing some of the same, but focused on photometric redshifts. The key idea is to make the galaxy type, luminosity, and redshift priors hierarchically; that is, to use the data on many galaxies to construct the best priors to use for each individual galaxy. Any such system makes photometric redshift predictions but also makes strong predictions or precise measurements of many other things, including galaxy metallicities, star-formation rates, and properties as a function of redshift and environment.
One of the things we discussed, which definitely requires a lot more research, is the idea of hybrid methods between supervised and unsupervised. Imagine that you have a training set, but the training set is incomplete, small, or unreliable. Then you want to generate your priors using a mixture of the training data and all the data. Hierarchical methods can be trained on all the data with no supervision—no training set—but they can also be trained with supervision, so hierarchical methods are the best (or simplest, anyway) places to look at hybrid training.
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