Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Pushing the bounds of dropout   We show that dropout training is best understood as performing MAP estimation
concurrently for a family of conditional models whose objectives are themselves
lower bounded by the original dropout objective. This discovery allows us to
pick any model from this family after training, which leads to a substantial
improvement on regularisation-heavy language modelling. The family includes
models that compute a power mean over the sampled dropout masks, and their less
stochastic subvariants with tighter and higher lower bounds than the fully
stochastic dropout objective. We argue that since the deterministic
subvariant's bound is equal to its objective, and the highest amongst these
models, the predominant view of it as a good approximation to MC averaging is
misleading. Rather, deterministic dropout is the best available approximation
to the true objective.

5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn