  This article introduces byteSteady -- a fast model for classification using
byte-level n-gram embeddings. byteSteady assumes that each input comes as a
sequence of bytes. A representation vector is produced using the averaged
embedding vectors of byte-level n-grams, with a pre-defined set of n. The
hashing trick is used to reduce the number of embedding vectors. This input
representation vector is then fed into a linear classifier. A straightforward
application of byteSteady is text classification. We also apply byteSteady to
one type of non-language data -- DNA sequences for gene classification. For
both problems we achieved competitive classification results against strong
baselines, suggesting that byteSteady can be applied to both language and
non-language data. Furthermore, we find that simple compression using Huffman
coding does not significantly impact the results, which offers an
accuracy-speed trade-off previously unexplored in machine learning.
