EP-IT Data Science Seminars
Approximate Inference and Deep Generative Models
by
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Europe/Zurich
500/1-001 - Main Auditorium (CERN)
Description
Advances in deep generative models are at the forefront of deep learning research because of the promise they offer for allowing data-efficient learning, and for model-based reinforcement learning. In this talk I'll review a few standard methods for approximate inference and introduce modern approximations which allow for efficient large-scale training of a wide variety of generative models. Finally, I'll demonstrate several important application of these models to density estimation, missing data imputation, data compression and planning.
Organised by
M. Girone, M. Elsing, L. Moneta, M. Pierini.......... Refreshments will be served at 10h30
Webcast
There is a live webcast for this event