Some of the most important deep learning papers
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Some of the most important deep learning papers
1–10 of 17 posts
Re: Some of the most important deep learning papers
#2I really enjoyed this write up .. would be so great if research papers in general were commented on like this (what is interesting, what is the significance of the result etc)
Re: Some of the most important deep learning papers
#3Good list! I think it's important to note that this article is (intentionally) focused on modern CNN architectures, and not "deep learning" in general.
I'd also add in the following "technique" articles: Geoff Hinton et al.'s dropout paper[0] and Loffe and Szegedy's Batch Normalization paper[1]. I don't think there's been enough time for the dust to settle, but I'm excited about the possibilities Stochastic Depth[2] could offer, too.
[0]: http://arxiv.org/abs/1207.0580 [1]: http://arxiv.org/abs/1502.03167 [2]: http://arxiv.org/abs/1603.09382
Re: Some of the most important deep learning papers
#4Only applied to image recognition.
Re: Some of the most important deep learning papers
#5Glad to see R-CNN and its follow-on work on the list. We've been using R-CNN for a few weeks now and have seen great results on object detection and localization. A few papers this year have played around with substituting different convnets and different classification schemes and improving the network in various ways. I'm excited to see where this specific architecture goes in the next few years.
Re: Some of the most important deep learning papers
#6Here is the same list but including another important paper, inception-v4, it beat MS resnets on top 5 error.
https://github.com/andrewt3000/MachineLearning/blob/master/c...
Re: Some of the most important deep learning papers
#7We've compiled a fairly extensive list of deep learning papers here: http://deeplearning4j.org/deeplearningpapers
We also Tweet out new ones as they're published here: https://twitter.com/deeplearning4j
Re: Some of the most important deep learning papers
#8With this field advancing so fast, I guess if we could do something like this, that would be great:
Maintain a running list of:
- 3-5 most important papers in the last 3 months
- 3-5 in the last 1 year (not all in the 3 month list would make into this list)
- 3-5 in the last 5 years.
I guess it's difficult for a small number of people to rank the papers. Maybe a hackernews or reddit style upvote/downvote system can be used, with a list that essentially scrapes arxiv for papers.
Re: Some of the most important deep learning papers
#9Only applied to image recognition.
The "Generating Image Descriptions (2014)" paper also involves text.
Re: Some of the most important deep learning papers
#10With this field advancing so fast, I guess if we could do something like this, that would be great: Maintain a running list of: - 3-5 most important papers in the last 3 months - 3-5 in the last 1 year (not all in the 3 month list would make into this list) - 3-5 in the last 5 years. I guess it's difficult for a small number of people to rank the papers. Maybe a hackernews or reddit style upvote/downvote system can b…
> I guess it's difficult for a small number of people to rank the papers.
This is what's done every year at the AI conferences. No need for a new voting system.
- 3-5 most important papers in the last 3 months: The "best paper award" deep learning papers of the most recent 1-2 AI conferences.
- 3-5 in the last 1 year: Top cited deep learning papers from AI conferences this year.
- 3-5 in the last 5 years: Top cited deep learning papers from AI conferences in the last 5 years.
Since AI conferences don't happen every day (or even every month) this would not be a hard list for a novice to maintain.