
Flow-Based Deep Generative Models - sacheendra
https://lilianweng.github.io/lil-log/2018/10/13/flow-based-deep-generative-models.html
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ziont
what can i use this for?

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jimfleming
From the article:

> A good estimation of p(x) makes it possible to efficiently complete many
> downstream tasks: sample unobserved but realistic new data points (data
> generation), predict the rareness of future events (density estimation),
> infer latent variables, fill in incomplete data samples, etc.

To give an example, we use generative models for model-based reinforcement
learning. Basically, RL algorithms have relatively poor sample efficiency.
They require a lot more data than supervised learning for a number of reasons.
Rather than train an agent against real data, which is limited, we train a
generative model using supervised learning and use the model to simulate real
data—either using the generated outputs or the latent representations. This
covers several of the author's described use cases.

