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We begin our study into generative modeling with autoregressive models. As before, we assume we are given access to a dataset of -dimensional datapoints . For simplicity, we assume the datapoints are binary, i.e., .

Representation

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We continue our study over another type of likelihood based generative models. As before, we assume we are given access to a dataset of -dimensional datapoints . So far we have learned two types of likelihood based generative models:

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    We now move onto another family of generative models called generative adversarial networks (GANs). GANs are unique from all the other model families that we have seen so far, such as autoregressive models, VAEs, and normalizing flow models, because we do not train them using maximum likelihood.

    Likelihood-free learning

    diff --git a/docs/index.html b/docs/index.html index 3c1c7c5..236e4b1 100644 --- a/docs/index.html +++ b/docs/index.html @@ -18,7 +18,47 @@ - + + - -

    These notes form a concise introductory course on deep generative models. They are based on Stanford CS236, taught by Stefano Ermon and Aditya Grover, and have been written by Aditya Grover, with the help of many students and course staff. The notes are still under construction! diff --git a/docs/introduction/index.html b/docs/introduction/index.html index f0f5990..c9b062f 100644 --- a/docs/introduction/index.html +++ b/docs/introduction/index.html @@ -18,7 +18,47 @@ - + + - -

    Intelligent agents are constantly generating, acquiring, and processing data. This data could be in the form of images that we capture on our phones, text messages we share with our friends, graphs that model diff --git a/docs/vae/index.html b/docs/vae/index.html index c9d2f14..8a3615d 100644 --- a/docs/vae/index.html +++ b/docs/vae/index.html @@ -18,7 +18,47 @@ - + + - -