Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GOPHER: Categorical probabilistic forecasting with graph structure via
  local continuous-time dynamics   We consider the problem of probabilistic forecasting over categories with
graph structure, where the dynamics at a vertex depends on its local
connectivity structure. We present GOPHER, a method that combines the inductive
bias of graph neural networks with neural ODEs to capture the intrinsic local
continuous-time dynamics of our probabilistic forecasts. We study the benefits
of these two inductive biases by comparing against baseline models that help
disentangle the benefits of each. We find that capturing the graph structure is
crucial for accurate in-domain probabilistic predictions and more sample
efficient models. Surprisingly, our experiments demonstrate that the continuous
time evolution inductive bias brings little to no benefit despite reflecting
the true probability dynamics.

33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat