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A Few months ago I was faced with a problem, my model is built on machine learning algorithms simply have not worked. I've thought long and hard how to solve this problem and at some point realized that my knowledge is very limited, and ideas are scarce. I know a couple of dozen models, and it is a very small part of those works which can be very useful.
the First thought that came to mind is that if I know and understand more patterns, my qualities as a researcher and engineer in General, will increase. This idea led me to study articles of recent conferences on machine learning. Structure this information is quite difficult and you need to record the dependencies and relationships between the methods. I didn't want to introduce dependencies in the form of a table or list, but I wanted something more natural. In the end, I realized that to have a three-dimensional graph with edges between the models and their components, looks pretty interesting.
for Example, the architectural GAN  consists of a generator (GEN) and discriminator (DIS), the Adversarial Avtandilovich (AAE)  consists of Avtandilovich (AE) , and DIS. Each component is a single vertex in this graph, so for AAE, we will have the edge with the AE and DIS.
step by Step, I analyzed the articles discharged from any of the methods they contain, in what subject area they apply to, what data they tested, and so on. In the process, I realized how many very interesting solutions remain unknown, and do not find their application.
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