GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamicsDownload PDF

Published: 18 Oct 2021, Last Modified: 05 May 2023ICBINB@NeurIPS2021 SpotlightReaders: Everyone
Keywords: probabilistic forecasting, neural ODEs, graph neural networks, robustness, marked temporal point processes
TL;DR: learning dynamics in an node embedding space does not capture true dynamics in probability space
Abstract: 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.
Category: Stuck paper: I hope to get ideas in this workshop that help me unstuck and improve this paper
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