5 ms·

Grid-Cell Like Deep Layers in Navigation AI

77 points by SubiculumCode 8y ago

baylearn 8y ago
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 https://arxiv.org/abs/1803.07770 Published at ICLR 2018, a well-known machine learning conference: https://openreview.net/forum?id=B17JTOe0- 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.
- autopoiesis 8y ago
  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...
  - SubiculumCode 8y ago
    Was the arxiv paper ever peer reviewed?
    - baylearn 8y ago
      Yes, refer to the openreview link to my earlier comment on this thread.
- yokaze 8y ago
  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.
  - naturalgradient 8y ago
    If you are making that argument, you should refer to the ICLR deadline, which is well earlier, but still after July 2017.
  - apl 8y ago
    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).
- brolover 8y ago
  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.
- musicaldope 8y ago
  Additional context for the overlap with previous work on grid cells in neuroscience: https://twitter.com/gershbrain/status/994678940043284480 https://twitter.com/gershbrain/status/994678940043284480
- justicezyx 8y ago
  Thanks for point out this! I'd rather read the similar work from ICLR than nature, which I do not have access...
inarrears 8y ago
The link says I have to pay $8.99 to "rent" the article, or subscribe every year for $199. WTF? Is this science? Why can't people publish research in open access journals or conferences?
- wei_jok 8y ago
  Unfortunately a lot of researchers submit themselves to closed access journals due to prestige factors. I understand why a PhD student or academic might do this, to rake up impact factor points in order to graduate or get pass the tenure committee. But these guys are DeepMind, with high salary jobs already, without the academic BS, so you'd figure they would know better ...
  - hestefisk 8y ago
    This is Nature, the single most prestigious journal on science. It’s the Ivy League of PhDs. A publication in that journal is a guaranteed job safety in university circles.
    - zerostar07 8y ago
      maybe if you are a biologist/neuroscientist. but for machine learnign research, how sure are we?
      - nabla9 8y ago
        We are. Nature has enormous prestige across all fields. It has the highest H-index. Nature (the multidisciplinary journal) should not be confused with other Nature publications that have the name "Nature" in them, like Nature Neurscience or Nature Reviews Molecular Cell Biology.
        zerostar07 8y ago
        What do you mean? H-index applies to individual researchers. All nature journals have high impact factors - and readership among hard scientists, but i dont think ML researchers read Nature every month. I understand that some reserachers need the prestige factor. But honestly , in the case of deep mind, i think they are giving prestige to Nature as an ML journal, where they previously had none. They could have published their results in a reputable open access journal like eLife instead, and do a service to the open access community. Publishing closed access is not a great direction to move towards. Also, why didn't they pay for Nature's open access option, it's not like they don't have the money.
goldenkey 8y ago
Hiearchichal Temporal Memory is exactly what this is. HTM has been successfully used to create artificial hypocampus. Watch the video in the first link below and try not to be impressed. Links: https://discourse.numenta.org/t/oscillatory-thousand-brains-minds-eye-for-htm/3726 https://discourse.numenta.org/t/oscillatory-thousand-brains-... https://en.m.wikipedia.org/wiki/Hierarchical_temporal_memory https://en.m.wikipedia.org/wiki/Hierarchical_temporal_memory

77 points by SubiculumCode 8y ago

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 https://arxiv.org/abs/1803.07770 Published at ICLR 2018, a well-known machine learning conference: https://openreview.net/forum?id=B17JTOe0- 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.

autopoiesis 8y ago
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...
- SubiculumCode 8y ago
  Was the arxiv paper ever peer reviewed?
  - baylearn 8y ago
    Yes, refer to the openreview link to my earlier comment on this thread.
yokaze 8y ago
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.
- naturalgradient 8y ago
  If you are making that argument, you should refer to the ICLR deadline, which is well earlier, but still after July 2017.
- apl 8y ago
  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).
brolover 8y ago
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.
musicaldope 8y ago
Additional context for the overlap with previous work on grid cells in neuroscience: https://twitter.com/gershbrain/status/994678940043284480 https://twitter.com/gershbrain/status/994678940043284480
justicezyx 8y ago
Thanks for point out this! I'd rather read the similar work from ICLR than nature, which I do not have access...

inarrears 8y ago

The link says I have to pay $8.99 to "rent" the article, or subscribe every year for $199. WTF? Is this science? Why can't people publish research in open access journals or conferences?

wei_jok 8y ago
Unfortunately a lot of researchers submit themselves to closed access journals due to prestige factors. I understand why a PhD student or academic might do this, to rake up impact factor points in order to graduate or get pass the tenure committee. But these guys are DeepMind, with high salary jobs already, without the academic BS, so you'd figure they would know better ...
- hestefisk 8y ago
  This is Nature, the single most prestigious journal on science. It’s the Ivy League of PhDs. A publication in that journal is a guaranteed job safety in university circles.
  - zerostar07 8y ago
    maybe if you are a biologist/neuroscientist. but for machine learnign research, how sure are we?
    - nabla9 8y ago
      We are. Nature has enormous prestige across all fields. It has the highest H-index. Nature (the multidisciplinary journal) should not be confused with other Nature publications that have the name "Nature" in them, like Nature Neurscience or Nature Reviews Molecular Cell Biology.
      - zerostar07 8y ago
        What do you mean? H-index applies to individual researchers. All nature journals have high impact factors - and readership among hard scientists, but i dont think ML researchers read Nature every month. I understand that some reserachers need the prestige factor. But honestly , in the case of deep mind, i think they are giving prestige to Nature as an ML journal, where they previously had none. They could have published their results in a reputable open access journal like eLife instead, and do a service to the open access community. Publishing closed access is not a great direction to move towards. Also, why didn't they pay for Nature's open access option, it's not like they don't have the money.

goldenkey 8y ago

Hiearchichal Temporal Memory is exactly what this is. HTM has been successfully used to create artificial hypocampus. Watch the video in the first link below and try not to be impressed. Links: https://discourse.numenta.org/t/oscillatory-thousand-brains-minds-eye-for-htm/3726 https://discourse.numenta.org/t/oscillatory-thousand-brains-... https://en.m.wikipedia.org/wiki/Hierarchical_temporal_memory https://en.m.wikipedia.org/wiki/Hierarchical_temporal_memory