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Found 8 papers, 0 papers with code
Meta-Learning for Neural Network-based Temporal Point Processes
The point process is widely used in many applications to predict such events related to human activities.
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains
Since the supports may have various granularities depending on attributes (e. g., poverty rate and crime rate), modeling such data is not straightforward.
Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes
Hawkes processes offer a central tool for modeling the diffusion processes, in which the influence from the past events is described by the triggering kernel.
Non-approximate Inference for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm
First we show that the MAP inference problem can be formulated as a (non-linear) minimum cost flow problem.
Probabilistic Optimal Transport based on Collective Graphical Models
In this study, we propose a new framework in which OT is considered as a maximum a posteriori (MAP) solution of a probabilistic generative model.
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs
By deriving the posterior GP, we can predict the data value at any location point by considering the spatial correlations and the dependences between areal data sets, simultaneously.
Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information
Though many point processes have been proposed to model events in a continuous spatio-temporal space, none of them allow for the consideration of the rich contextual factors that affect event occurrence, such as weather, social activities, geographical characteristics, and traffic.
Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities
With the proposed model, a distribution for each auxiliary data set on the continuous space is modeled using a Gaussian process, where the representation of uncertainty considers the levels of granularity.
