Re:Benchmarking TPU, GPU, and CPU Platforms for Deep Learning
by piadodjanho on Sunday July 28, 2019 @10:53PM
(#20551181)
There is a lot of text commented out in the original files [1]. I reckon they were concerned about the length of the paper. Unfortunately, the original images are still quite small.
> do yourself a favor and make the major conclusions graphically large!
Or add the data and the script to plot them in the gzip file you submit to arxiv.
Re:Benchmarking TPU, GPU, and CPU Platforms for Deep Learning
by mcnichol on Sunday July 28, 2019 @09:08PM
(#20550754)
It would be awesome if there were a video channel where folks reviewed these articles in a forum style.
Recognize this likely happens naturally in academia and at conferences but for those of us outside it all, it would (hopefully) raise the level of conversation as opposed to puzzling through some of it.
Likely challenging because so many starting points at different places. I can just imagine a twitch stream with someone reading through providing their thoughts.
Re:Benchmarking TPU, GPU, and CPU Platforms for Deep Learning
by hanrelan on Monday July 29, 2019 @01:03AM
(#20551648)
This youtube channel[1] may be what you're looking for. It has presentations of popular ML papers followed by some Q&A. It seems to target a technical but non-academic audience.
Re:Benchmarking TPU, GPU, and CPU Platforms for Deep Learning
by partingshots on Sunday July 28, 2019 @10:42PM
(#20551141)
I mean it's not like academia is some closed off circle or anything. Especially so within the field of computer science, from the very beginning, everything has always been publicly released and open source on arXiv.
Other fields are quickly improving as well, not to disparage anyone here, with many switching to arXiv in recent years too.
Re:Benchmarking TPU, GPU, and CPU Platforms for Deep Learning
by mcnichol on Tuesday July 30, 2019 @08:27AM
(#20564170)
I wasn't trying to indicate there was a barrier to getting the information. I think the ramp to understanding the information or at least the fact people consume information in different ways it's something I was interested in.
Think more modern-day Homebrew Club. That's what I was getting at
If you're going to write a paper like this to share such results, do yourself a favor and make the major conclusions graphically large!
Every one of Figures 8-12 could take up 1/2 a page and not be inappropriate. It's arxiv - what's the benefit of economizing?
> do yourself a favor and make the major conclusions graphically large!
Or add the data and the script to plot them in the gzip file you submit to arxiv.
[1] https://arxiv.org/format/1907.10701
Recognize this likely happens naturally in academia and at conferences but for those of us outside it all, it would (hopefully) raise the level of conversation as opposed to puzzling through some of it.
Likely challenging because so many starting points at different places. I can just imagine a twitch stream with someone reading through providing their thoughts.
[1] https://www.youtube.com/channel/UCfk3pS8cCPxOgoleriIufyg
Other fields are quickly improving as well, not to disparage anyone here, with many switching to arXiv in recent years too.
Think more modern-day Homebrew Club. That's what I was getting at