FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels
Authors: Guillaume Staerman, Cédric Allain, Alexandre Gramfort, Thomas Moreau
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | the statistical and computational efficiency of the novel approach is demonstrated through various numerical experiments. Finally, the method s effectiveness is evaluated by modeling the occurrence of stimuli-induced patterns from brain signals recorded with magnetoencephalography (MEG). |
| Researcher Affiliation | Academia | 1Universit e Paris-Saclay, Inria, CEA, Palaiseau, 91120, France. Correspondence to: Guillaume Staerman <guillaume.staerman@inria.fr>. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using and implementing some features from third-party libraries (tick, pyhawkes, alphacsc) but does not provide a concrete statement or link for the open-source code of their proposed Fa DIn method. |
| Open Datasets | Yes | Experiments on MEG data were run on two datasets from the MNE Python package (Gramfort et al., 2013; 2014): the sample dataset and the somatosensory (somato) dataset2. 2Both available at https://mne.tools/stable/ overview/datasets_index.html |
| Dataset Splits | No | The paper does not provide specific details on how the datasets were split into training, validation, or test sets, such as percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, memory specifications, or cloud computing instance types used for running the experiments. |
| Software Dependencies | No | The paper mentions several software packages (tick, pyhawkes, alphacsc, MNE Python) and algorithms (RMSprop) used in the experiments but does not specify their version numbers. |
| Experiment Setup | Yes | The parameter W is set to 1. This experiment is repeated for varying values of T {103, 104, 105}. ... The parameter W of Fa DIn is set to 1. ... The RMSprop algorithm is used in Fa DIn. ... The discretization size of the non-parametric kernel is settled as in Fa DIn. ... with a grid discretization equal to data re-sampling rate of 150 Hz (i.e., δ = 1/150). |