christ that paragraph took two pages
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autoencoding => "the process of determining a worldview and a canon in developing a method of mimesis" okay then there's a section on how autoencoders work but i think i'm better off reading the wikipedia article due to my cs background https://en.wikipedia.org/wiki/Autoencoder …
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so autoencoders mathematically compress inputs to their most efficient signal:noise ratio the paper translates this to mean that they create likenesses of their inputs, in order to get the gist across without spending too many resources this is about right
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there's a joke about the authors of the paper being good autoencoders here somewhere
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okay now they're explaining data science and machine learning 101 the explanation passes my sniff test, looks like they aren't talking out of their asses actually a pretty good explanation, i highly recommend reading it if you want to know the super basics of how ml algos work
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lots of "hey are you even allowed to make mathematical abstractions of real world things? turns out: yes" i repeat: this is a good intro to data science stuff for non-data science people
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so one big thing autoencoders do is dimensional reduction this means they draw out the most salient features of a type of thing, and what best differentiates examples from each other in order to understand an aesthetic, one must do a dim. red. on it
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Replying to @imhinesmi
can you explain this a bit more? i’m not entirely clear on what ‘cognitive systems’ means in this context. (i also haven’t gotten to this point in the paper yet)
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Replying to @theactualNPH
A cognitive system is anything that thinks or does thinking-like things, i.e. people and autoencoders
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So everyone has a "canon" of things they can reconstruct 100% right, and things align with their aesthetic more the closer they are to the canon
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