Neural Spectral Marked Point Processes
Authors: Shixiang Zhu, Haoyun Wang, Zheng Dong, Xiuyuan Cheng, Yao Xie
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section presents experimental results on both synthetic and real data and compares them with several state-of-the-arts, including (i) standard Hawkes process with an exponentially decaying kernel function (Hawkes) (Hawkes, 1971); (ii) recurrent marked temporal point processes (RMTPP) (Du et al., 2016); and (iii) Neural Hawkes process (NH) (Mei & Eisner, 2017). |
| Researcher Affiliation | Academia | Shixiang Zhu , Haoyun Wang , Zheng Dong , Xiuyuan Cheng , Yao Xie Georgia Institute of Technology Duke University |
| Pseudocode | Yes | Algorithm 1: Stochastic gradient-based learning algorithm; Algorithm 2: Monte Carlo estimation for the integral of conditional intensity in (6); Algorithm 3: Efficient thinning algorithm for simulating point process |
| Open Source Code | Yes | Codes to reproduce the experimental results are publicly available1. 1https://github.com/meowoodie/Neural-Spectral-Marked-Point-Processes |
| Open Datasets | Yes | Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset (2014) that comes from broadband, short period, strong motion seismic sensors, GPS, and other geophysical sensors. |
| Dataset Splits | No | The paper specifies training and testing splits but does not explicitly mention a separate validation set split. For synthetic data: 'We fit the models using 80% of the synthetic data set and the remaining 20% as the testing set.' For real data: 'We fit the models using 80% of the data set and the remaining 20% as the testing set.' |
| Hardware Specification | Yes | All experiments are performed on Google Colaboratory (Pro version) with 12GB RAM and dual-core Intel processors, which speed up to 2.3 GHz (without GPU). |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer Kingma & Ba (2014)' and 'Soft Plus' activation function, but it does not specify version numbers for general software dependencies like Python, PyTorch, TensorFlow, or specific libraries beyond their names. |
| Experiment Setup | Yes | We set the rank R = 5 in our setting. We consider the shared network that summarizes input data into a hidden embedding to be a fully connected three-layer network and the sub-network to be a fully connected network with two hidden layers. The width of the hidden layers in the shared network is n = 128, and the width of the input layers in sub-networks (or the output layer in the shared network) is p = 10. We adopt the Soft Plus f(x) = 1/ log(1 + exp(x)) as the activation function of each layer in the network. To learn the model s parameters, we adopt the Adam optimizer Kingma & Ba (2014) with a constant learning rate of 10 2 and the batch size is 32. |