~6% sparsity + the way that you think about data representation is very interesting. I will have to have a shot at running the autoencoder over the Mikolov 3m w

~6% sparsity + the way that you think about data representation is very interesting. I will have to have a shot at running the autoencoder over the Mikolov 3m word corpus. My goal is to get the first 1m words compressed at under 100 MB zipped, including all indexes, which currently allows us to distribute the vectors for free at github without paying for data transfer. (To date about 8000 words have been mapped through the words2map Google API, while I haven't really begun to do anything interesting here yet...)