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Comment by baylearn | Hacker News Reader

This DeepMind work has been gathering a lot of hype, but it is very similar to this work by Cueva and Wei:

Published on Arxiv on March 31: https://arxiv.org/abs/1803.07770

Published at ICLR 2018, a well-known machine learning conference: https://openreview.net/forum?id=B17JTOe0-

It is rather disheartening that they didn't acknowledge the -- seems like a deliberate oversight.

The Cueva and Wei paper is well written, explains concepts clearly, without the hype, and published in an open access venue.

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Yes, this DeepMind paper has been associated with an extraordinary amount of hype! This must be the third or fourth time it has been posted here, when most of the extensive grid cell literature has never been posted once; including theoretical work that explains the emergence of such cells, or that uses grids for navigation. And I don't think this is the first time that a DeepMind paper has been selective in its literature review, either...

Was the arxiv paper ever peer reviewed?

Yes, refer to the openreview link to my earlier comment on this thread.

I've noticed that DeepMind completely ignores imitation learning area of research. I never saw them citing any of the fundamental papers, despite a bunch of their reinforcement learning ideas explicitly match those ideas that are now 8+ years old.

Well, the paper was received from Nature on 05 July 2017, so it isn't surprising that they didn't cite the work from Cueva and Wei published 21 Mar 2018.

That's not quite how the process works. These papers go through multiple (> 2) revisions. At any iteration, there's ample opportunity for updating references. This applies double given how long the Cueva/Wei work has been available (preprint & CCN'17 (?) contribution).

If you are making that argument, you should refer to the ICLR deadline, which is well earlier, but still after July 2017.

Additional context for the overlap with previous work on grid cells in neuroscience:

https://twitter.com/gershbrain/status/994678940043284480

Thanks for point out this!

I'd rather read the similar work from ICLR than nature, which I do not have access...

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