You are all cordially invited to the AMLab seminar on Tuesday **April 3** at 16:00 in C3.163, where **Karen Ullrich** will give a talk titled “**Variational Bayes Wake-Sleep algorithm for expressive latent representations in 3D protein reconstruction**”. Afterwards there are the usual drinks and snacks!

**Abstract**: Reconstructing three dimensional structures from noisy two dimensional orthographic projections is a central task in many scientific domains, examples range from medical tomography to single particle electron microscopy.

We treat this problem from a Bayesian point of view. Specifically, we regard a specimen’s structure and its pose as latent factors which are marginalized over. This allows us to express uncertainty in pose and even local uncertainty in the sample’s structure. This information can serve to detect unstable sub-structures or multiple configurations of a specimen. In particular, we apply amortized deep neural networks to encode observations into latent factors. This bears the advantage of transferability across multiple structures. To this end, we propose to train the model alternately in observation space and latent space, resulting in a generalized version of the wake-sleep algorithm.

We focus our experiments on cryogenic electron microscopy (CryoEM) single particle analysis, a technique that enables deep understanding of structural biology and chemistry by inspecting single proteins. We show our model to be competitive while predicting reasonable uncertainties. Moreover, we empirically demonstrate that the model is more data efficient than competitive methods and that it is transferable between molecules.