A Survey of Deep Learning for Scientific Discovery
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Re: A Survey of Deep Learning for Scientific Discovery
#2Looks like a good summary. Will read. But at the rate the discipline moves I feel like we need one of these every couple of months for everyone (not just "lay" scientists). Anyone know a good journal or something that produces a similar sort of survey frequently? Like once a quarter?
Re: A Survey of Deep Learning for Scientific Discovery
#3In a survey on "scientific discovery", I would have expected more examples than face and image recognition and natural language processing, which are so stale at this point.
Healthcare? Physics? Chemistry? Biology? Sociology?
Re: A Survey of Deep Learning for Scientific Discovery
#4Eric Schmidt, as in Google's ex-CEO, is the second author of this paper! I didn't know he did any scientific research.
Re: A Survey of Deep Learning for Scientific Discovery
#5Eric Schmidt, as in Google's ex-CEO, is the second author of this paper! I didn't know he did any scientific research.
He has a PhD, unlike Brin and Page.
Re: A Survey of Deep Learning for Scientific Discovery
#6Looks like a good summary. Will read. But at the rate the discipline moves I feel like we need one of these every couple of months for everyone (not just "lay" scientists). Anyone know a good journal or something that produces a similar sort of survey frequently? Like once a quarter?
“Rate at which the discipline moves” is mostly churn, not progress. Important insights come at a slower rate — at the speed of human understanding, not at the speed of conference papers. Good papers from even decades ago are likely to still be useful — in fact, they will have the key ideas presented simply and clearly, without much jargon or hype. Yes, deep learning practice moves quite fast these days, but that’s just the veneer on top of those deeper ideas, trying out tweaks and variations. That’s not completely an indictment of deep learning, rather, any nascent field has a lot of confusing bustle.
Re: A Survey of Deep Learning for Scientific Discovery
#7Re: A Survey of Deep Learning for Scientific Discovery
#8Always wonder who these kinds of reviews / surveys are for? Nobody is going to learn machine learning by reading a 50 page pdf. Meanwhile, people that have experience will have a hard time finding the info they don't already know.
Opinionated & narrow >> Shallow & comprehensive
Re: A Survey of Deep Learning for Scientific Discovery
#9Always wonder who these kinds of reviews / surveys are for? Nobody is going to learn machine learning by reading a 50 page pdf. Meanwhile, people that have experience will have a hard time finding the info they don't already know. Opinionated & narrow >> Shallow & comprehensive
They are useful for someone in a nearby field trying to learn this field. That person first reads the textbook, and a few specific papers. Then once, they have a good narrow understanding, they broaden it by reading one of these review papers.
In essence, a review paper saves you the trouble of doing a literature review in a new subfield, because it identifies the important papers for you.
That said, the reason review papers are usually written is for the authors to cement their own understanding of the network of research in the field.
Re: A Survey of Deep Learning for Scientific Discovery
#10Always wonder who these kinds of reviews / surveys are for? Nobody is going to learn machine learning by reading a 50 page pdf. Meanwhile, people that have experience will have a hard time finding the info they don't already know. Opinionated & narrow >> Shallow & comprehensive
A good review article is worth its weight in gold for both the researchers who write it and the research community.
Remember that research communities are extremely transient because of the professor : phd student : practitioner ratio and the low odds that a graduated phd student a) stays in research and then b) stays in the same research area for their whole career. Therefore, most members of a given research community have approximately 1-3 years of experience in the broader academic field and approximately no experience in the area covered by the review. Therefore, a good review can simultaneously:
1. prevent a lot of wheel re-invention, and
2. push the research field in a certain direction (either accidentally or purposefully).
Also, good review articles typically include some amount of synthesis. I.e., the creation of a conceptual framework and language for understanding and talking about a bunch of vaguely related stuff. This article tries to do that e.g. in Section 2.1 but the topic of the review is so incredibly broad that the categories are not super useful.