Abstract
This code is a collection of python code that defines, trains, and tests Log-Additive Convolutional Neural Networks. The model components and training routine are based on the PyTorch python library. The code implements the Log-Additive Convolutional Neural Networks as described in Pagendam et al. 2023. In addition to the Log-Additive Convolutional Neural Networks, this library also defines the Log-Normal Density loss function as described in Pagendam et al. 2023. Code from this paper is not publicly available, so the Pytorch implementation of this type of model is unique to this library.
- Developers:
- Release Date:
- 2024-05-16
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOE National Nuclear Security Administration (NNSA)Primary Award/Contract Number:AC52-06NA25396
- Code ID:
- 140120
- Site Accession Number:
- O4752
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Country of Origin:
- United States
Citation Formats
Callis, Skylar.
PyTorch Implementation of Log-Additive Convolutional Neural Networks.
Computer Software.
https://github.com/lanl/pagendam-callis-groundwaterML.
USDOE National Nuclear Security Administration (NNSA).
16 May. 2024.
Web.
doi:10.11578/dc.20240809.8.
Callis, Skylar.
(2024, May 16).
PyTorch Implementation of Log-Additive Convolutional Neural Networks.
[Computer software].
https://github.com/lanl/pagendam-callis-groundwaterML.
https://doi.org/10.11578/dc.20240809.8.
Callis, Skylar.
"PyTorch Implementation of Log-Additive Convolutional Neural Networks." Computer software.
May 16, 2024.
https://github.com/lanl/pagendam-callis-groundwaterML.
https://doi.org/10.11578/dc.20240809.8.
@misc{
doecode_140120,
title = {PyTorch Implementation of Log-Additive Convolutional Neural Networks},
author = {Callis, Skylar},
abstractNote = {This code is a collection of python code that defines, trains, and tests Log-Additive Convolutional Neural Networks. The model components and training routine are based on the PyTorch python library. The code implements the Log-Additive Convolutional Neural Networks as described in Pagendam et al. 2023. In addition to the Log-Additive Convolutional Neural Networks, this library also defines the Log-Normal Density loss function as described in Pagendam et al. 2023. Code from this paper is not publicly available, so the Pytorch implementation of this type of model is unique to this library.},
doi = {10.11578/dc.20240809.8},
url = {https://doi.org/10.11578/dc.20240809.8},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20240809.8}},
year = {2024},
month = {may}
}