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I've been reading the SQAIR paper lately, and the mathematics involved seems a bit complicated.

Some background, about the paper: SQAIR stands for Sequential Attend, Infer, Repeat - the paper does generative modelling of moving objects. The idea of Attend, Infer, Repeat is to decompose a static scene into constituent objects, where each object is represented by continuous latent variables. The latent variables, $z^{what}$,$z^{where}$ and $z^{pres}$ encode the appearance, position and presence of an object.

Here's a screenshot of the first of many things I'm unable to understand -

enter image description here

Why is $z^{pres,1:n+1}$ a random vector of $n$ ones followed by a zero? Why do we need the zero? How does it help?

Furthermore, an explanation of equation $(2)$ as in the image above, would be great.

P.S. I hope you all find the paper interesting. I'll ask other questions from the paper in separate posts, so as to not crowd one post with too many queries.

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From my understanding of the paper, $Z^{pres}$ keeps track of the objects in the scene. For every step of the sequential inference, $z^{pres,i}$ takes either a 0 or a 1. A 1 represents that an object is present and it has to be explained by the remaining latent variables. 0 indicates that all the objects have been explained and inference is complete.

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