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ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting
A spatio-temporal physics-coupled neural network (ST-PCNN) model is proposed to achieve three goals: (1) learning the underlying physics parameters, (2) transition of local information between spatio-temporal regions, and (3) forecasting future values for the dynamical system.
Semisupervised Learning on Heterogeneous Graphs and its Applications to Facebook News Feed
We show that the HELP algorithm improves the predictive performance across multiple tasks, together with semantically meaningful embedding that are discriminative for downstream classification or regression tasks.
Validation of Matching
We introduce a technique to compute probably approximately correct (PAC) bounds on precision and recall for matching algorithms.
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