Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Moving Deep Learning into Web Browser: How Far Can We Go?   Recently, several JavaScript-based deep learning frameworks have emerged,
making it possible to perform deep learning tasks directly in browsers.
However, little is known on what and how well we can do with these frameworks
for deep learning in browsers. To bridge the knowledge gap, in this paper, we
conduct the first empirical study of deep learning in browsers. We survey 7
most popular JavaScript-based deep learning frameworks, investigating to what
extent deep learning tasks have been supported in browsers so far. Then we
measure the performance of different frameworks when running different deep
learning tasks. Finally, we dig out the performance gap between deep learning
in browsers and on native platforms by comparing the performance of
TensorFlow.js and TensorFlow in Python. Our findings could help application
developers, deep-learning framework vendors and browser vendors to improve the
efficiency of deep learning in browsers.

21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
19
The categories of this paper are: eess