From 21f900b5d6f301df39c52e570b6143a85407da4e Mon Sep 17 00:00:00 2001 From: kanghoon Date: Tue, 25 Aug 2020 15:00:54 +0900 Subject: [PATCH] readme --- readme.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 readme.md diff --git a/readme.md b/readme.md new file mode 100644 index 0000000..e0a076d --- /dev/null +++ b/readme.md @@ -0,0 +1,18 @@ +# Fully Neural Network based Model for General Temporal Point Process(Neurips 2019,Takahiro Omi) + +This code is pytorch version of implementation for Neural Temporal Point Process. + +Temporal Point Process is mathematical model for capturing patterns of discrete event occurrences. + However, the traditional point process have limited expressivity by assumption. + For example, Poisson process assumes the independence of all events though it changes by time. + Other point process like Hawkes process does not assume the independence but the intensity function + of Hawkes process should be positive and have exponential decaying kernel. For these reason, the author + suggest the generalized version of point process by introducing neural network. + + +Reference + +github : https://github.com/omitakahiro/NeuralNetworkPointProcess + + +