Probabilistic deep learning methods have recently made great progress for generative and discriminative modeling. I will give a brief overview of recent developments and then present two contributions. The first is on a generalization of generative adversarial networks (GAN), extending their use considerably. GANs can be shown to approximately minimize the Jensen-Shannon divergence between two distributions, the true sampling distribution and the model distribution. We extend GANs to the class of f-divergences which include popular divergences such as the Kullback-Leibler divergence. This enables applications to variational inference and likelihood-free maximum likelihood, as well as enables GAN models to become basic building blocks in larger models. The second contribution is to consider representation learning using variational autoencoder models. To make learned representations of data useful we need ground them in semantic concepts. We propose a generative model that can decompose an observation into multiple separate latent factors, each of which represents a separate concept. Such disentangled representation is useful for recognition and for precise control in generative modeling. We learn our representations using weak supervision in the form of groups of observations where all samples within a group share the same value in a given latent factor. To make such learning feasible we generalize recent methods for amortized probabilistic inference to the dependent case. Joint work with: Ryota Tomioka (MSR Cambridge), Botond Cseke (MSR Cambridge), Diane Bouchacourt (Oxford)

**Biography: **Sebastian is an expert in Probabilistic Deep Learning and Structured Prediction. Among others, Sebastian's work on structured prediction models for pattern recognition tasks has shown that highly expressive probabilistic models can still be computationally tractable. More recently, his work on the theory and applications of deep generative models has advanced both the theory and practical aspects of the field. Sebastian is a former member of the Empirical Inference Department and is now a Principal Researcher at the Machine Intelligence and Perception Group at Microsoft Research Cambridge.

- 29 May 2017 • 12:00 - 13:00
- Max Planck House Lecture Hall
- Empirical Inference