Research Papers In Computer Science

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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.

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.

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.

Treaty Of Paris Essay - Research Papers In Computer Science

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.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.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.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.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.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.


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