A Survey of Deep Learning for Scientific Discovery

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.

The rest you listed require inference and causality.

Deep learning does not do this.

Data with less noises are what most deep learning and non statistical models does well. Meaning that image, nlp, etc.. deep learning does well. But data with lots noises/uncertainty/variance or even data that isn't large enough, such as time series, currently statistical models are still king (https://en.wikipedia.org/wiki/Makridakis_Competitions).

Even with healthcare you're answering a question/ hypothesis. This is where statistical models strength lies because all statistical models are hypothesis tests and vice versa. There are very little opportunity in healthcare where you would use deep learning compare to statistic. I've seen NLP can be of use but the majority of work in healthcare are inference/casuality base (this is why they use propensity model so much). I'm in this space public healthcare.

He has a PhD, unlike Brin and Page.

“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.

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.

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.

I will read it, to defend my non-DeepLearning choices for supervised ML .. so many on the bandwagon for unsupervised CNN with their GPUs