Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Invariance Learning in Deep Neural Networks with Differentiable Laplace
  Approximations   Data augmentation is commonly applied to improve performance of deep learning
by enforcing the knowledge that certain transformations on the input preserve
the output. Currently, the correct data augmentation is chosen by human effort
and costly cross-validation, which makes it cumbersome to apply to new
datasets. We develop a convenient gradient-based method for selecting the data
augmentation. Our approach relies on phrasing data augmentation as an
invariance in the prior distribution and learning it using Bayesian model
selection, which has been shown to work in Gaussian processes, but not yet for
deep neural networks. We use a differentiable Kronecker-factored Laplace
approximation to the marginal likelihood as our objective, which can be
optimised without human supervision or validation data. We show that our method
can successfully recover invariances present in the data, and that this
improves generalisation on image datasets.

2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics