Seminario di Informatica: “Advances in deep generative models”

Mercoledì 20 Giugno 2018, ore 12:00 - Aula 2BC60 - Nicola De Cao


Mercoledì 20 Giugno 2018 alle ore 12:00 in Aula 2BC60, Nicola De Cao (University of Amsterdam) terrà un seminario dal titolo “Advances in deep generative models”.

Deep Generative Models (DGMs) use unsupervised learning techniques to build a samplable function that matches any data distribution. The primary objective of a DGM is to learn the true data distribution from a set of data-points such that, subsequently, it is possible to generate new data points from a parametrized learned approximation of it. They have achieved enormous success in the past few years thanks to the use of deep learning techniques which allowed to learn effective distribution approximations and led to impressive results and applications in many fields. We will provide a brief introduction of two of the most commonly used approaches for designing DGMs namely: Variational Auto-Encoders (VAE) and Generative Adversarial Networks (GAN). We further introduce two recent advances in this field such as Hyperspherical VAE and MolGAN (an implicit model for drugs generation).

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