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The fundamental 'unit' of NN computation is not an individual vector element but rather an entire vector. One of the first results you often learn about in linear algebra is that some axes are more important than others (principal components, singular value decomposition). Thus, it totally stands to reason that the underlying field of the vector is inconsequential but rather the entire vector machinery. All you have to do is make sure that there are enough elements in the vector to get the job done for whatever bit size of element.



I see, so the idea is that enough of the quantization errors are sort of averaged out across the dimensions of the vector space to still be useful?


The way I think about it is finally it will end in a binary feature vector similar to 20Questions (male or female, alive or dead ...) just with 100s of dimensions




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