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My analysis focused on our second largest discipline, Computer Science. 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. Click on any graph to explore it in more detail or to grab the raw data.
A minority of Computer Scientists have listed a subdiscipline. I would encourage everyone to do so.
Latent Dirichlet Allocation available full-text LDA is a means of classifying objects, such as documents, based on their underlying topics. 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.
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.
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. 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.
AR is the futuristic idea most familiar to the average sci-fi enthusiast as Terminator-vision. 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.
Toward the next generation of recommender systems: 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.
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.
Professor Boyd has a very popular set of video classes at Stanford on the subject, which probably gave this a little boost, as well.
Videos of techniques at SciVee or JoVE or recorded lectures previously can really help spread awareness of your research. 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. Well, there are a few things to note.IITM Student Team, a NIRMAAN pre-incubatee, won the Second Place in the international event, MoveHack, the Global Mobility Hackathon conducted by NITI Aayog, Govt.
of India. The 20th century was nearly into its fourth decade before the first electronic computer came along, and those early machines were behemoths capable of only the most basic tasks.
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