You are all cordially invited to the AMLab seminar on **Thursday 14th Feb** at **16:00** in C3.163, where **Victor Garcia** will give a talk titled “**GRIN: Graphical Recurrent Inference Networks**“. Afterwards there are the usual drinks and snacks!

**Abstract:** A graphical model is a structured representation of the data generating process. The traditional method to reason over random variables is to perform inference in this graphical model. However, in many cases the generating process is only a poor approximation of the much more complex true data generation process, leading to poor posterior estimates. The subtleties of the generative process are however captured in the data itself and we can “learn to infer”, that is, learn a direct mapping from observations to explanatory latent variables. In this work we propose a hybrid model that combines graphical inference with a learned inverse model, which we structure as a graph neural network. The iterative algorithm is formulated as a recurrent neural network. By using cross-validation we can automatically balance the amount of work performed by graphical inference versus learned inference. We apply our ideas to the Kalman filter, a Gaussian hidden Markov model for time sequences. We apply our “Graphical Recurrent Inference” method to a number of path estimation tasks and show that it successfully outperforms either learned or graphical inference run in isolation.