[200 words is a rough guide for the length of a summary. Feel free to go a fair bit over or under if needs be. The editor will fix any issues with images being rendered too wide/narrow etc. See README for how to view locally if you wish to (not required. Contact CB if this is broken for you.)]

The key idea

A few sentences outlining why the paper is interesting…

Add images where appropriate throughout. This section should always have at least 1 key figure though.

Please use high-res images (zoom in for those screenshots!)

A specific and succinct sentence or two describing the figure (alt text). Valuable for seo and accessibility.

Figure 1a. If the caption isn't included in the image, it should be added like so.

[optional] Background

If necessary, a short intro to background matierial needed to understand the method

Their method

Latex can be included in the standard way, either inline: $R=\sum {t=0}^{\infty }\gamma ^{t}r{t}$

Or as a block:

$$ Q_{t+1}^{A}(s_{t},a_{t})=Q_{t}^{A}(s_{t},a_{t})+\alpha _{t}(s_{t},a_{t})\left(r_{t}+\gamma Q_{t}^{B}\left(s_{t+1},\mathop {\operatorname {arg~max} } _{a}Q_{t}^{A}(s_{t+1},a)\right)-Q_{t}^{A}(s_{t},a_{t})\right). $$

Code can also be included in the standard way:

import popart

builder = popart.Builder()

# Build a simple graph
i1 = builder.addInputTensor(popart.TensorInfo("FLOAT", [1, 2, 32, 32]))
i2 = builder.addInputTensor(popart.TensorInfo("FLOAT", [1, 2, 32, 32]))

o = builder.aiOnnx.add([i1, i2])

Results

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[optional] Takeaways

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