RIP. If it's any consolation, it sounds like the list is at least three years old by now. Which is a long time considering that 2016 is generally regarded as the date of the deep learning revolution.
In my experience when it comes to learning technical subjects from a position of relative total ignorance, it's the older resources that are the easiest to bootstrap knowledge from. Then you basically work your way forward through the newer texts, like an accelerated replay of a domain's progress.
I think it's kind of obvious that this would be the case when you think about it. Just like how history textbooks can't keep growing in size to give all past events an equal treatment, nor can technical references as a domain matures.
You're forced to toss out stuff deemed least relevant to today, and in technical domains that's often stuff you've just started assuming as understood by the reader... where early editions of a new space would have prioritized getting the reader up to speed in something totally novel to the world.
I thought it was 2012, when AlexNet took the imagenet crown?
(Gosh, it's been less than a decade. Time sometimes doesn't fly, considering how much it's changed the world since then...)
I’m just trying to imagine the things you could do with it back then. 2007 had relatively fast gpus for the time, but certainly nothing compared to today. Yet it’d certainly be enough for MNIST training, which makes me wonder what else could be done.
Here are some papers with many citations:
- An empirical evaluation of deep architectures on problems with many factors of variation [2]
- Extracting and composing robust features with denoising autoencoders [3]
- Scaling learning algorithms towards AI [4]
[1] https://scholar.google.com/citations?hl=en&user=kukA0LcAAAAJ...
[2] https://scholar.google.com/citations?view_op=view_citation&h...
[3] https://scholar.google.com/citations?view_op=view_citation&h...
[4] https://scholar.google.com/citations?view_op=view_citation&h...