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Found 5 papers, 2 papers with code
Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimization
However, such simple kernel specifications are deficient in learning functions with complex features, such as being nonstationary, nonseparable, and multimodal.
Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data
Probabilistic modeling of multidimensional spatiotemporal data is critical to many real-world applications.
Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging
Specifically, we propose a novel spatial aggregation network (SAN) inspired by Principal Neighborhood Aggregation, which uses multiple aggregation functions to help one node gather diverse information from its neighbors.
Scalable Spatiotemporally Varying Coefficient Modelling with Bayesian Kernelized Tensor Regression
To address this challenge, we summarize the spatiotemporally varying coefficients using a third-order tensor structure and propose to reformulate the spatiotemporally varying coefficient model as a special low-rank tensor regression problem.
Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation
In this paper, we propose a low-rank autoregressive tensor completion (LATC) framework by introducing \textit{temporal variation} as a new regularization term into the completion of a third-order (sensor $\times$ time of day $\times$ day) tensor.
