[HN Gopher] A Survey of Deep Learning for Scientific Discovery
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       A Survey of Deep Learning for Scientific Discovery
        
       Author : alokrai
       Score  : 53 points
       Date   : 2020-03-27 17:46 UTC (5 hours ago)
        
 (HTM) web link (arxiv.org)
 (TXT) w3m dump (arxiv.org)
        
       | jefft255 wrote:
       | Eric Schmidt, as in Google's ex-CEO, is the second author of this
       | paper! I didn't know he did any scientific research.
        
         | hervature wrote:
         | He has a PhD, unlike Brin and Page.
        
           | jefft255 wrote:
           | Right, but they both were Ph.D. students and Brin I think
           | published quite a bit of scientific papers before dropping
           | out.
        
       | throwqwerty wrote:
       | Looks 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?
        
         | ssivark wrote:
         | "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.
        
       | biomodel wrote:
       | Always 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
        
         | abdullahkhalids wrote:
         | 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.
        
         | throwawayjava wrote:
         | 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.
        
       | antipaul wrote:
       | In 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?
        
         | p1esk wrote:
         | This is just a bad title. Should have been named simply as "A
         | Survey of Deep Learning". This paper is an excellent and up to
         | date overview of deep learning models, methods and best
         | practices.
        
       | wswin wrote:
       | for the moment I thought it was from 2003
        
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