Building Machines that Imagine and Reason.pdf

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2021-02-24

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Shakir Mohamed

@shakir_za

shakir@google.com

Building Machines that

Imagine and Reason

Principles and Applications of Deep Generative Models

Deep Learning Summer School

August 2016

Machines that Imagine and Reason

Abstract

Building Machines that Imagine and Reason:

Principles and Applications of Deep Generative Models

Deep generative models provide a solution to the problem of unsupervised learning, in which a machine

learning system is required to discover the structure hidden within unlabelled data streams. Because they are

generative, such models can form a rich imagery the world in which they are used: an imagination that can

harnessed to explore variations in data, to reason about the structure and behaviour of the world, and

ultimately, for decision-making. This tutorial looks at how we can build machine learning systems with a

capacity for imagination using deep generative models, the types of probabilistic reasoning that they make

possible, and the ways in which they can be used for decision making and acting.

Deep generative models have widespread applications including those in density estimation, image denoising

and in-painting, data compression, scene understanding, representation learning, 3D scene construction, semi-

supervised classiﬁcation, and hierarchical control, amongst many others. After exploring these applications,

we'll sketch a landscape of generative models, drawing-out three groups of models: fully-observed models,

transformation models, and latent variable models. Different models require different principles for inference

and we'll explore the different options available. Different combinations of model and inference give rise to

different algorithms, including auto-regressive distribution estimators, variational auto-encoders, and

generative adversarial networks. Although we will emphasise deep generative models, and the latent-variable

class in particular, the intention of the tutorial is to explore the general principles, tools and tricks that can be

used throughout machine learning. These reusable topics include Bayesian deep learning, variational

approximations, memoryless and amortised inference, and stochastic gradient estimation. We'll end by

highlighting the topics that were not discussed, and imagine the future of generative models.

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