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Reading Computer Science Research Paper

Since we recently announced our $10001 Binary Battle to promote applications built on the Mendeley API (now including PLoS as well), I decided to take a look at the data to see what people have to work with. My analysis focused on our second largest discipline, Computer Science. Biological Sciences (my discipline) is the largest, but I started with this one so that I could look at the data with fresh eyes, and also because it’s got some really cool papers to talk about. Here’s what I found:

What I found was a fascinating list of topics, with many of the expected fundamental papers like Shannon’s Theory of Information and the Google paper, a strong showing from Mapreduce and machine learning, but also some interesting hints that augmented reality may be becoming more of an actual reality soon.

The top graph summarizes the overall results of the analysis. This graph shows the Top 10 papers among those who have listed computer science as their discipline and chosen a subdiscipline. The bars are colored according to subdiscipline and the number of readers is shown on the x-axis. The bar graphs for each paper show the distribution of readership levels among subdisciplines. 17 of the 21 CS subdisciplines are represented and the axis scales and color schemes remain constant throughout. Click on any graph to explore it in more detail or to grab the raw data.(NB: A minority of Computer Scientists have listed a subdiscipline. I would encourage everyone to do so.)

1. Latent Dirichlet Allocation (available full-text)

LDA is a means of classifying objects, such as documents, based on their underlying topics. I was surprised to see this paper as number one instead of Shannon’s information theory paper (#7) or the paper describing the concept that became Google (#3). It turns out that interest in this paper is very strong among those who list artificial intelligence as their subdiscipline. In fact, AI researchers contributed the majority of readership to 6 out of the top 10 papers. Presumably, those interested in popular topics such as machine learning list themselves under AI, which explains the strength of this subdiscipline, whereas papers like the Mapreduce one or the Google paper appeal to a broad range of subdisciplines, giving those papers a smaller numbers spread across more subdisciplines. Professor Blei is also a bit of a superstar, so that didn’t hurt. (the irony of a manually-categorized list with an LDA paper at the top has not escaped us)

2. MapReduce : Simplified Data Processing on Large Clusters (available full-text)

It’s no surprise to see this in the Top 10 either, given the huge appeal of this parallelization technique for breaking down huge computations into easily executable and recombinable chunks. The importance of the monolithic “Big Iron” supercomputer has been on the wane for decades. The interesting thing about this paper is that had some of the lowest readership scores of the top papers within a subdiscipline, but folks from across the entire spectrum of computer science are reading it. This is perhaps expected for such a general purpose technique, but given the above it’s strange that there are no AI readers of this paper at all.

3. The Anatomy of a large-scale hypertextual search engine (available full-text)

In this paper, Google founders Sergey Brin and Larry Page discuss how Google was created and how it initially worked. This is another paper that has high readership across a broad swath of disciplines, including AI, but wasn’t dominated by any one discipline. I would expect that the largest share of readers have it in their library mostly out of curiosity rather than direct relevance to their research. It’s a fascinating piece of history related to something that has now become part of our every day lives.

4. Distinctive Image Features from Scale-Invariant Keypoints

This paper was new to me, although I’m sure it’s not new to many of you. This paper describes how to identify objects in a video stream without regard to how near or far away they are or how they’re oriented with respect to the camera. AI again drove the popularity of this paper in large part and to understand why, think “Augmented Reality“. AR is the futuristic idea most familiar to the average sci-fi enthusiast as Terminator-vision. Given the strong interest in the topic, AR could be closer than we think, but we’ll probably use it to layer Groupon deals over shops we pass by instead of building unstoppable fighting machines.

5. Reinforcement Learning: An Introduction (available full-text)

This is another machine learning paper and its presence in the top 10 is primarily due to AI, with a small contribution from folks listing neural networks as their discipline, most likely due to the paper being published in IEEE Transactions on Neural Networks. Reinforcement learning is essentially a technique that borrows from biology, where the behavior of an intelligent agent is is controlled by the amount of positive stimuli, or reinforcement, it receives in an environment where there are many different interacting positive and negative stimuli. This is how we’ll teach the robots behaviors in a human fashion, before they rise up and destroy us.

6. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (available full-text)

Popular among AI and information retrieval researchers, this paper discusses recommendation algorithms and classifies them into collaborative, content-based, or hybrid. While I wouldn’t call this paper a groundbreaking event of the caliber of the Shannon paper above, I can certainly understand why it makes such a strong showing here. If you’re using Mendeley, you’re using both collaborative and content-based discovery methods!

7. A Mathematical Theory of Communication (available full-text)

Now we’re back to more fundamental papers. I would really have expected this to be at least number 3 or 4, but the strong showing by the AI discipline for the machine learning papers in spots 1, 4, and 5 pushed it down. This paper discusses the theory of sending communications down a noisy channel and demonstrates a few key engineering parameters, such as entropy, which is the range of states of a given communication. It’s one of the more fundamental papers of computer science, founding the field of information theory and enabling the development of the very tubes through which you received this web page you’re reading now. It’s also the first place the word “bit”, short for binary digit, is found in the published literature.

8. The Semantic Web (available full-text)

In The Semantic Web, Tim Berners-Lee, Sir Tim, the inventor of the World Wide Web, describes his vision for the web of the future. Now, 10 years later, it’s fascinating to look back though it and see on which points the web has delivered on its promise and how far away we still remain in so many others. This is different from the other papers above in that it’s a descriptive piece, not primary research as above, but still deserves it’s place in the list and readership will only grow as we get ever closer to his vision.

9. Convex Optimization (available full-text)

This is a very popular book on a widely used optimization technique in signal processing. Convex optimization tries to find the provably optimal solution to an optimization problem, as opposed to a nearby maximum or minimum. While this seems like a highly specialized niche area, it’s of importance to machine learning and AI researchers, so it was able to pull in a nice readership on Mendeley. Professor Boyd has a very popular set of video classes at Stanford on the subject, which probably gave this a little boost, as well. The point here is that print publications aren’t the only way of communicating your ideas. Videos of techniques at SciVee or JoVE or recorded lectures (previously) can really help spread awareness of your research.

10. Object recognition from local scale-invariant features (available in full-text)

This is another paper on the same topic as paper #4, and it’s by the same author. Looking across subdisciplines as we did here, it’s not surprising to see two related papers, of interest to the main driving discipline, appear twice. Adding the readers from this paper to the #4 paper would be enough to put it in the #2 spot, just below the LDA paper.


So what’s the moral of the story? Well, there are a few things to note. First of all, it shows that Mendeley readership data is good enough to reveal both papers of long-standing importance as well as interesting upcoming trends. Fun stuff can be done with this! How about a Mendeley leaderboard? You could grab the number of readers for each paper published by members of your group, and have some friendly competition to see who can get the most readers, month-over-month. Comparing yourself against others in terms of readers per paper could put a big smile on your face, or it could be a gentle nudge to get out to more conferences or maybe record a video of your technique for JoVE or Khan Academy or just Youtube.

Another thing to note is that these results don’t necessarily mean that AI researchers are the most influential researchers or the most numerous, just the best at being accounted for. To make sure you’re counted properly, be sure you list your subdiscipline on your profile, or if you can’t find your exact one, pick the closest one, like the machine learning folks did with the AI subdiscipline. We recognize that almost everyone does interdisciplinary work these days. We’re working on a more flexible discipline assignment system, but for now, just pick your favorite one.

These stats were derived from the entire readership history, so they do reflect a founder effect to some degree. Limiting the analysis to the past 3 months would probably reveal different trends and comparing month-to-month changes could reveal rising stars.

Technical details:
To do this analysis I queried the Mendeley database, analyzed the data using R, and prepared the figures with Tableau Public. A similar analysis can be done dynamically using the Mendeley API. The API returns JSON, which can be imported into R using the fineRJSONIO package from Duncan Temple Lang and Carl Boettiger is implementing the Mendeley API in R. You could also interface with the Google Visualization API to make motion charts showing a dynamic representation of this multi-dimensional data. There’s all kinds of stuff you could do, so go have some fun with it. I know I did.

Like this:


How to Read a Technical Paper

by Jason Eisner (2009)

Multi-pass reading

Skim the paper first, skipping over anything that would take much mental effort. Just get an idea of where the paper is going, why it was written, what's old hat and what's new to you. To force yourself to keep moving, give yourself a limited time budget per page or use the autoscroll feature of your PDF reader.

Now, assuming the paper still seems worthwhile, go back and read the whole thing more carefully.

Why not practice on this webpage? Go ahead, skim it first.

S. Keshav describes three-pass reading in detail: What are you trying to do on each pass?

Write as you read

Write as you read. This keeps your attention focused and makes you engage with the paper.

Low-level notes

Often it is easiest to scribble notes on the printed-out paper itself, responding in context to the formulas, figures, and text. In that case, file or scan your annotated copy for future reference.

(Or perhaps annotate the PDF file directly, without printing or scanning. A free alternative to Acrobat is PDF-XChange Viewer, a Windows program that can also be run on Linux via wine. A free native Linux option is Xournal.)

You can use notes on the paper to

  • restate unclear points in your own words
  • fill in missing details (assumptions, algebraic steps, proofs, pseudocode)
  • annotate mathematical objects with their types
  • come up with examples that illustrate the author's ideas, and examples that would be problematic for the author
  • draw connections to other methods and problems you know about
  • ask questions about things that aren't stated or that don't make sense
  • challenge the paper's claims or methods
  • dream up followup work that you (or someone) should do

High-level notes

Low-level notes aren't enough. Also keep high-level notes about papers. You should try to distill the paper down: summarize the things that interested you, contrast with other papers, and record your own questions and ideas for future work. Writing this distillation gives you a goal while reading the paper, and the notes will be useful to you later.

Michael Mitzenmacher writes: "Read creatively. Reading a paper critically is easy, in that it is always easier to tear something down than to build it up. Reading creatively involves harder, more positive thinking. What are the good ideas in this paper? Do these ideas have other applications or extensions that the authors might not have thought of? Can they be generalized further? Are there possible improvements that might make important practical differences? If you were going to start doing research from this paper, what would be the next thing you would do?"

At a minimum, you should re-explain the ideas in your own words: produce some text that is aimed at your future self. You should be able to reread this later and quickly reconstruct your understanding of the paper. Don't waste time repeating the parts that are easy for you. Include a URL to the original paper, and refer as needed to the paper's Figure 1, equation (2), section 3.3, etc. But do spend time writing down hard-won bits of understanding:

"They don't say this, but equation (2) is basically the same as the method of Pookie (2001), except that they add a reconfabulation step after the data purée. I was surprised at their reconfabulator, which doesn't match what I would have expected from Kachu (2004), but it does cure the exponential growth problem in this domain. To see the difference, I found it useful to think about this example: ..."

Organizing your notes

I suggest sorting your file of notes chronologically, by when you read the paper, since that may help you find vaguely remembered papers or remember what else you were reading at the time. Sometimes you'll want to search by author/title/etc., so start the notes for each paper with a rough citation. (See also How to Organize Your Files.)

If you had to put a lot of effort into really understanding some point, you can share that effort with others (and record it for your own future reference) by improving the discussion of that point on the relevant Wikipedia page.

Many people have devised software or personal systems for annotating papers and keeping track of notes. Quora users give their recommendations here and here.

When and where to read

Start early. Leave enough time that if your attention wanders, you can put the paper down and pick it up again when you're in a better reading mood. This is better than trying to force yourself through it on a deadline.

Some people find it easier to read at particular times of day, or while eating or walking or riding an exercise bike. Do you habitually pick up the closest thing to read when you're at the breakfast table or in the bathroom? Then leave papers there for yourself.

Try reading with a friend! Sit next to each other, looking at the same copy of the paper, and stay synchronized at the paragraph or sentence level. Read aloud at times. You'll keep each other moving and help each other through the hard parts. Discuss as you go along.

Set aside time

When you are starting out in a new area, it may take you hours to read a conference paper thoroughly. That's okay. It's worth spending that much time to really understand a good or foundational paper. It will pay off in your future reading and research.

I'll never find the time! Don't worry. Not all papers take that long. Many ideas are reused across papers, so you will get faster at reading. By now, in an area I know well, I can often read a paper in 30 minutes or less, because the motivation is familiar and I can recognize much of the setup as standard practice. (After all, most papers fall into an existing tradition. They extend existing work with one or two genuine new ideas, and some supporting details that may or may not be significant.)

But I'm already a third-year student. Why is this paper taking me so long? There is no shame in reading slowly. It still takes me several hours to absorb a paper on something that I genuinely don't know well. (Also, it takes me hours to review a paper even in my own area, because the burden is on me to spot all the problems or opportunities for improvement. 75% of submitted conference papers are rejected, and most of the remaining 25% also need improvement before publication.)

Which parts to focus on

So do you really have to read the whole paper carefully on your second pass? Sometimes, but not always. It depends on why you're reading the paper.

I do think that when you are learning a new area, you should read at least some papers extremely thoroughly. That means knowing what every sentence and every subscript is doing, so that you really learn all of the techniques used in the paper. And understanding why things were done as they were: ask yourself dumb questions and answer them. Practice the ability to decode the entire paper—as if you were reviewing it critically and trying to catch any errors, sloppy thinking, or incompleteness. This will sharpen your critical thinking. You will want to turn this practiced critical eye on yourself as you plan, execute, and write up your own research.

However, there will also be occasional papers where it is not worth reading all the details right now. Maybe the details are of limited interest, or you simply don't feel equipped to understand them yet. Consider the parts of a typical paper:

  • Motivation. You'll want to understand this fairly well, or there's no point in reading the paper at all. But part of the motivation may depend on things you don't know (mathematical background or past work). If you don't want to chase those references down now, you could just raise their priority on your reading list.

  • Mathematics and algorithms. These parts are the technical heart of the paper. So don't make a habit of skimming them. (You can learn a lot from how the authors solved their problems.) Nonetheless, you might skim a technical section if

    • It seems like an explanation of something you already know. In that case, just check that it really says what you think.

    • While you probably would benefit right away from knowing the method in detail, this paper is just not a good place to learn it, or it is too advanced for you right now. Understand what you reasonably can, and then put it on your list of things to learn for real. Perhaps ask someone else to explain it to you or to recommend a reading.

    • It seems like an ugly ad hoc solution that no one would ever want to use anyway. The only reason to understand it fully would be if you wanted to criticize it or improve upon it. (Still, even if you skip the ugly details, understand what the authors' intuitions were. Think about how to capture those intuitions more elegantly.)

    • It's enough to know for now that the method exists. It seems specialized, so you might never need it. You'll come back to the paper if you do.

      But you should still achieve clarity now about what the method accomplishes (its interface). Also try to glean when it is applicable, how hard it would be to use, and what determines its runtime and accuracy. Then you'll remember the method when you need it.

      What you might skip for now are the hard parts: the internal workings of the method (its implementation) and any proofs of correctness or efficiency.

  • Experiments. Many papers test their methods empirically. When you're new to a field, you should examine carefully how this is typically done (and whether you approve!). It can also be helpful to notice what datasets and code were used—as you may want to use them yourself in future.

    But once you've learned the ropes, you may not always care so much about a paper's experiments. After all, sometimes you're only reading the paper to stoke your creativity with some new problems or techniques. I confess that I often pay less attention to the experimental details—though examples or error analysis do catch my attention because they often shed light.

    If you do care about the conclusions of the paper ("did the method work?" "should I use it?"), then you should go back and carefully examine the experimental design, including the choice of data. Were the experiments fair? Do they support the claims? What's really going on? Are the conclusions likely to generalize beyond this experimental scenario?

In short, invest your time wisely. Focus on what is valuable to take away. If you can't figure out which parts of the paper are most "interesting" or "important," do ask someone who should know! If you don't know who to ask, find other papers that cite this one (via Google Scholar) and see what they say about this paper.

Delip Rao suggests: "Never read the original paper on X first. Instead read several later papers on what they say about X, get an idea of X and then read the original paper. Somehow the research community is much better in explaining ideas clearly than the original authors themselves."

What to read

  • do creative web search
    • experiment with several searches
    • put yourself in an author's shoes; what phrases might they have used?
    • become a power searcher! (read the help pages for your search engine)
    • specifically search at the ACL Anthology, Google Scholar, etc.

  • track down related work (once you've got a relevant paper)
    • backward references: follow the bibliography to earlier papers
    • forward references: see who else has cited the work (via an interface such as Google Scholar)

  • has someone else already listed the right papers for you?
    • survey papers in journals (also called "review articles")
    • course syllabi
    • reading group webpages
    • chapters in textbooks
    • online tutorials
    • literature review chapters from dissertations
    • direct recommendations from friends or professors (perhaps at other institutions)

  • breadth-first exploration
    • read a lot of abstracts (and skim the papers as needed) before deciding which papers are best to read
    • it's okay to read multiple related papers at once, flipping back and forth so that they clarify one another
    • to get a feel for the research landscape in an area, flip through the proceedings of a relevant recent workshop, conference, or special-theme journal issue

  • when the going gets tough, switch to background reading
    • textbooks or tutorials
    • review articles
    • introductions and lit review chapters from dissertations
    • early papers that are heavily cited
    • sometimes Wikipedia

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