Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Implicit Bias of Dropout   Algorithmic approaches endow deep learning systems with implicit bias that
helps them generalize even in over-parametrized settings. In this paper, we
focus on understanding such a bias induced in learning through dropout, a
popular technique to avoid overfitting in deep learning. For single
hidden-layer linear neural networks, we show that dropout tends to make the
norm of incoming/outgoing weight vectors of all the hidden nodes equal. In
addition, we provide a complete characterization of the optimization landscape
induced by dropout.

21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an