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Since we recently announced our 001 Binary Battle to promote applications built on the Mendeley API (now including PLo S 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.
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.
If you’re using Mendeley, you’re using both collaborative and content-based discovery methods! 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.
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.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 .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.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.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.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.