Generative Modeling Summer School (GeMMS)Technical University of DenmarkGeneral course objectives: The summer school is targeted toward PhD students working with data science broadly and for whom generative modelling potentially plays a part in their projects. The objective of the course is to introduce the students to the basics behind the most widely used deep generative models as well as expose the students to cutting-edge research in deep generative models. Learning objectives: A student who has met the objectives of the course will be able to:
Contents: The course gives the students an introduction to the basics behind the most widely used generative modelling techniques and insights into the newest research on deep generative models and their applications. The course is accordingly divided into two parts. The first part (Monday-Tuesday-Wednesday) consists of lectures and practical lab sessions. The topics covered within the first three days are 1) introduction to generative modelling, 2) autoregressive models, 3) flow-based models, 4) probabilistic PCA, 5) deep latent variable models, 6) energy-based models, 7) generative adversarial networks and 8) probabilistic circuits. The reading material for this first part is the course book and code examples listed as course literature. A detailed reading list is given on the course homepage, and this is mandatory preparation for the course. The course organisers conduct the lectures in the first part, and teaching assistants aid the lab sessions. This part is concluded by a mandatory poster session, where the participant share their (planned) research and discuss how deep generative models can be applied in the given context. The second part (Thursday-Friday) will contain invited talks given by leading researchers in academia and industry. These talks will focus on both cutting-edge methodological developments in deep generative modelling and applications of deep generative modelling. |
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