Abstract
Tensor ELF is a user-friendly parallel tensor decomposition Python toolbox that includes a suite of machine learning algorithms for CPU and GPU architectures for the analysis of sparse and dense data including utility tools for pre-processing and post-processing.
- Developers:
- Release Date:
- 2024-04-25
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOE Laboratory Directed Research and Development (LDRD) ProgramPrimary Award/Contract Number:AC52-06NA25396
- Code ID:
- 127327
- Site Accession Number:
- C22048
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Country of Origin:
- United States
Citation Formats
Eren, Maksim, Alexandrov, Boian, Bhattarai, Manish, Rasmussen, Kim, Skau, Erik, Truong, Duc, and Djibrilla, Boureima.
Tensor Extraction of Latent Features (TELF).
Computer Software.
https://github.com/lanl/T-ELF.
USDOE Laboratory Directed Research and Development (LDRD) Program.
25 Apr. 2024.
Web.
doi:10.5281/zenodo.10257896.
Eren, Maksim, Alexandrov, Boian, Bhattarai, Manish, Rasmussen, Kim, Skau, Erik, Truong, Duc, & Djibrilla, Boureima.
(2024, April 25).
Tensor Extraction of Latent Features (TELF).
[Computer software].
https://github.com/lanl/T-ELF.
https://doi.org/10.5281/zenodo.10257896.
Eren, Maksim, Alexandrov, Boian, Bhattarai, Manish, Rasmussen, Kim, Skau, Erik, Truong, Duc, and Djibrilla, Boureima.
"Tensor Extraction of Latent Features (TELF)." Computer software.
April 25, 2024.
https://github.com/lanl/T-ELF.
https://doi.org/10.5281/zenodo.10257896.
@misc{
doecode_127327,
title = {Tensor Extraction of Latent Features (TELF)},
author = {Eren, Maksim and Alexandrov, Boian and Bhattarai, Manish and Rasmussen, Kim and Skau, Erik and Truong, Duc and Djibrilla, Boureima},
abstractNote = {Tensor ELF is a user-friendly parallel tensor decomposition Python toolbox that includes a suite of machine learning algorithms for CPU and GPU architectures for the analysis of sparse and dense data including utility tools for pre-processing and post-processing.},
doi = {10.5281/zenodo.10257896},
url = {https://doi.org/10.5281/zenodo.10257896},
howpublished = {[Computer Software] \url{https://doi.org/10.5281/zenodo.10257896}},
year = {2024},
month = {apr}
}