Web: http://arxiv.org/abs/2102.10271

June 17, 2022, 1:11 a.m. | Rui Wang, Robin Walters, Rose Yu

cs.LG updates on arXiv.org arxiv.org

Current deep learning models for dynamics forecasting struggle with
generalization. They can only forecast in a specific domain and fail when
applied to systems with different parameters, external forces, or boundary
conditions. We propose a model-based meta-learning method called DyAd which can
generalize across heterogeneous domains by partitioning them into different
tasks. DyAd has two parts: an encoder which infers the time-invariant hidden
features of the task with weak supervision, and a forecaster which learns the
shared dynamics of the …

arxiv dynamics forecasting inference learning lg meta meta-learning

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