Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Neural Network Training with Frank-Wolfe   This paper studies the empirical efficacy and benefits of using
projection-free first-order methods in the form of Conditional Gradients,
a.k.a. Frank-Wolfe methods, for training Neural Networks with constrained
parameters. We draw comparisons both to current state-of-the-art stochastic
Gradient Descent methods as well as across different variants of stochastic
Conditional Gradients. In particular, we show the general feasibility of
training Neural Networks whose parameters are constrained by a convex feasible
region using Frank-Wolfe algorithms and compare different stochastic variants.
We then show that, by choosing an appropriate region, one can achieve
performance exceeding that of unconstrained stochastic Gradient Descent and
matching state-of-the-art results relying on $L^2$-regularization. Lastly, we
also demonstrate that, besides impacting performance, the particular choice of
constraints can have a drastic impact on the learned representations.

34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat