trainablefrft
Source code for the experiments of Trainable Fractional Fourier Transform paper submitted to IEEE Signal Processing Letters.
Science Score: 75.0%
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✓CITATION.cff file
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✓codemeta.json file
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✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: ieee.org -
○Academic email domains
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Organization koc-lab has institutional domain (aykutkoclab.ee.bilkent.edu.tr) -
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Low similarity (9.3%) to scientific vocabulary
Keywords
Repository
Source code for the experiments of Trainable Fractional Fourier Transform paper submitted to IEEE Signal Processing Letters.
Basic Info
- Host: GitHub
- Owner: koc-lab
- Language: Python
- Default Branch: main
- Homepage: https://doi.org/10.1109/LSP.2024.3372779
- Size: 10.8 MB
Statistics
- Stars: 17
- Watchers: 2
- Forks: 2
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
Trainable Fractional Fourier Transform
In this repository, we present the source code for the experiments of our Trainable Fractional Fourier Transform paper is accepted to IEEE Signal Processing Letters. The installable package torch-frft is maintained at its own GitHub page. The package is available on both PyPI and Conda. Installation instructions are provided below. Please use the following BibTeX entry to cite our work:
bibtex
@article{trainable-frft-2024,
author = {Koç, Emirhan and Alikaşifoğlu, Tuna and Aras, Arda Can and Koç, Aykut},
journal = {IEEE Signal Processing Letters},
title = {Trainable Fractional Fourier Transform},
year = {2024},
volume = {31},
number = {},
pages = {751-755},
keywords = {Vectors;Convolution;Training;Task analysis;Computational modeling;Time series analysis;Feature extraction;Machine learning;neural networks;FT;fractional FT;deep learning},
doi = {10.1109/LSP.2024.3372779}
}
Installation of torch-frft
You can install the package directly from PyPI using pip or poetry as follows:
sh
pip install torch-frft
or
sh
poetry add torch-frft
or directly from Conda:
sh
conda install -c conda-forge torch-frft
Owner
- Name: Aykut Koç Lab
- Login: koc-lab
- Kind: organization
- Email: aykut.koc@bilkent.edu.tr
- Location: Turkey
- Website: http://aykutkoclab.ee.bilkent.edu.tr/
- Twitter: KocLab_Bilkent
- Repositories: 1
- Profile: https://github.com/koc-lab
Research group at Bilkent University focusing on machine learning and signal processing that extend into NLP and graph signal processing (GSP).
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
preferred-citation:
authors:
- family-names: Koç
given-names: Emirhan
- family-names: Alikaşifoğlu
given-names: Tuna
- family-names: Aras
given-names: Arda Can
- family-names: Koç
given-names: Aykut
doi: 10.1109/lsp.2024.3372779
identifiers:
- type: doi
value: 10.1109/lsp.2024.3372779
- type: url
value: http://dx.doi.org/10.1109/LSP.2024.3372779
- type: other
value: urn:issn:1070-9908
title: Trainable Fractional Fourier Transform
url: http://dx.doi.org/10.1109/LSP.2024.3372779
database: Crossref
date-published: 2024-03-04
year: 2024
issn: 1070-9908
journal: IEEE Signal Processing Letters
publisher:
name: Institute of Electrical and Electronics Engineers (IEEE)
start: '751'
end: '755'
type: article
volume: '31'
GitHub Events
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- Watch event: 7
Dependencies
- torch-frft *
- ipykernel ^6.25.0
- matplotlib ^3.7.2
- numpy ^1.25.1
- pandas ^2.0.3
- python >=3.10,<3.12
- scikit-learn ^1.3.0
- torch 2.0.0
- torch-frft ^0.6.1
- torchvision 0.15.1
- tqdm ^4.65.0
- wandb ^0.15.7
- appdirs ==1.4.4
- appnope ==0.1.3
- asttokens ==2.4.1
- backcall ==0.2.0
- certifi ==2023.7.22
- cffi ==1.16.0
- charset-normalizer ==3.3.1
- click ==8.1.7
- cmake ==3.27.7
- colorama ==0.4.6
- comm ==0.1.4
- contourpy ==1.1.1
- cycler ==0.12.1
- debugpy ==1.8.0
- decorator ==5.1.1
- docker-pycreds ==0.4.0
- exceptiongroup ==1.1.3
- executing ==2.0.0
- filelock ==3.13.0
- fonttools ==4.43.1
- gitdb ==4.0.11
- gitpython ==3.1.40
- idna ==3.4
- ipykernel ==6.26.0
- ipython ==8.16.1
- jedi ==0.19.1
- jinja2 ==3.1.2
- joblib ==1.3.2
- jupyter-client ==8.5.0
- jupyter-core ==5.4.0
- kiwisolver ==1.4.5
- lit ==17.0.3
- markupsafe ==2.1.3
- matplotlib ==3.8.0
- matplotlib-inline ==0.1.6
- mpmath ==1.3.0
- nest-asyncio ==1.5.8
- networkx ==3.2.1
- numpy ==1.26.1
- nvidia-cublas-cu11 ==11.10.3.66
- nvidia-cuda-cupti-cu11 ==11.7.101
- nvidia-cuda-nvrtc-cu11 ==11.7.99
- nvidia-cuda-runtime-cu11 ==11.7.99
- nvidia-cudnn-cu11 ==8.5.0.96
- nvidia-cufft-cu11 ==10.9.0.58
- nvidia-curand-cu11 ==10.2.10.91
- nvidia-cusolver-cu11 ==11.4.0.1
- nvidia-cusparse-cu11 ==11.7.4.91
- nvidia-nccl-cu11 ==2.14.3
- nvidia-nvtx-cu11 ==11.7.91
- packaging ==23.2
- pandas ==2.1.2
- parso ==0.8.3
- pathtools ==0.1.2
- pexpect ==4.8.0
- pickleshare ==0.7.5
- pillow ==10.1.0
- platformdirs ==3.11.0
- prompt-toolkit ==3.0.39
- protobuf ==4.24.4
- psutil ==5.9.6
- ptyprocess ==0.7.0
- pure-eval ==0.2.2
- pycparser ==2.21
- pygments ==2.16.1
- pyparsing ==3.1.1
- python-dateutil ==2.8.2
- pytz ==2023.3.post1
- pywin32 ==306
- pyyaml ==6.0.1
- pyzmq ==25.1.1
- requests ==2.31.0
- scikit-learn ==1.3.2
- scipy ==1.11.3
- sentry-sdk ==1.32.0
- setproctitle ==1.3.3
- setuptools ==68.2.2
- setuptools-scm ==8.0.4
- six ==1.16.0
- smmap ==5.0.1
- stack-data ==0.6.3
- sympy ==1.12
- threadpoolctl ==3.2.0
- tomli ==2.0.1
- torch ==2.0.0
- torch-frft ==0.6.1
- torchvision ==0.15.1
- tornado ==6.3.3
- tqdm ==4.66.1
- traitlets ==5.12.0
- triton ==2.0.0
- typing-extensions ==4.8.0
- tzdata ==2023.3
- urllib3 ==2.0.7
- wandb ==0.15.12
- wcwidth ==0.2.8
- wheel ==0.41.2