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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.
Fast Bayesian Inference for Gaussian Cox Processes via Path Integral Formulation
Gaussian Cox processes are widely-used point process models that use a Gaussian process to describe the Bayesian a priori uncertainty present in latent intensity functions.
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.
Histogram Meets Topic Model: Density Estimation by Mixture of Histograms
The histogram method is a powerful non-parametric approach for estimating the probability density function of a continuous variable.
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