Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Median Binary-Connect Method and a Binary Convolutional Neural Nework
  for Word Recognition   We propose and study a new projection formula for training binary weight
convolutional neural networks. The projection formula measures the error in
approximating a full precision (32 bit) vector by a 1-bit vector in the l_1
norm instead of the standard l_2 norm. The l_1 projector is in closed
analytical form and involves a median computation instead of an arithmatic
average in the l_2 projector. Experiments on 10 keywords classification show
that the l_1 (median) BinaryConnect (BC) method outperforms the regular BC,
regardless of cold or warm start. The binary network trained by median BC and a
recent blending technique reaches test accuracy 92.4%, which is 1.1% lower than
the full-precision network accuracy 93.5%. On Android phone app, the trained
binary network doubles the speed of full-precision network in spoken keywords
recognition.

2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc