Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Deep Gaussian Processes   This report provides an in-depth overview over the implications and novelty
Generalized Variational Inference (GVI) (Knoblauch et al., 2019) brings to Deep
Gaussian Processes (DGPs) (Damianou & Lawrence, 2013). Specifically, robustness
to model misspecification as well as principled alternatives for uncertainty
quantification are motivated with an information-geometric view. These
modifications have clear interpretations and can be implemented in less than
100 lines of Python code. Most importantly, the corresponding empirical results
show that DGPs can greatly benefit from the presented enhancements.

8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio