Input Selection for Bandwidth-Limited Neural Network Inference. (arXiv:1906.04673v2 [cs.LG] UPDATED)

Data are often accommodated on centralized storage servers. This is the case,
for instance, in remote sensing and astronomy, where projects produce several
petabytes of data every year. While machine learning models are often trained
on relatively small subsets of the data, the inference phase typically requires
transferring significant amounts of data between the servers and the clients.
In many cases, the bandwidth available per user is limited, which then renders
the data transfer to be one of the major …

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