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deepart

https://github.com/ap6yc/deepart

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
    Found CITATION.cff file
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    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
    Found 2 DOI reference(s) in README
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    Academic publication links
    Links to: zenodo.org
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    Academic email domains
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    JOSS paper metadata
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    Scientific vocabulary similarity
    Low similarity (14.1%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: AP6YC
  • License: cc0-1.0
  • Language: Julia
  • Default Branch: main
  • Size: 16.3 MB
Statistics
  • Stars: 0
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created almost 5 years ago · Last pushed about 1 year ago
Metadata Files
Readme License Code of conduct Citation

README.md

deepart-header

A repository containing implementations and experiments for the upcoming paper Deep Adaptive Resonance.

| Documentation | Testing Status | Zenodo DOI | |:-----------------:|:------------------:|:--------------:| | Docs | CI Status | DOI |

Table of Contents

Basic Usage

For detailed usage, please read the documentation.

The DeepART project is a Julia project, so its use follows typical Julia usage. The DeepART project also utilizes DrWatson.jl for organizing and running simulations.

The library code for the project is contained in src/, while all experiments are enumerated in scripts. Each folder therein contains a simple README for the order of running experiments.

For example, after installing Julia on your system, you instantiate this project with

julia using Pkg; Pkg.activate(); Pkg.instantiate()

and run an experiment interactively with

julia include("scripts/1_baselines/single/conv.jl")

or through the terminal with

shell julia --project="." "scripts/1_baselines/single/conv.jl"

Python

The Python component of this project can be installed on python=3.12 with

sh pip install -r requirements

or in editable mode with

sh pip install -e "./src/deepart

Attribution

Authors

Datasets

Assets

Quotes

To achieve great things, two things are needed: a plan and not quite enough time

-- Leonard Bernstein

Owner

  • Name: Sasha Petrenko
  • Login: AP6YC
  • Kind: user

Graduate researcher of applied computational intelligence at the Missouri University of Science and Technology.

Citation (CITATION.cff) 

title: "AP6YC/DeepART"
abstract: "A repository of code and data for the paper *Deep Adaptive Resonance*"
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
keywords:
  - "Adaptive Resonance Theory"
  - "Adaptive Resonance"
  - "Deep Hebbian Learning"
authors:
  - family-names: "Petrenko"
    given-names: "Sasha"
    orcid: https://orcid.org/0000-0003-2442-8901
    website: "https://ap6yc.github.io/"
    email: "sap625@mst.edu"
    alias: "AP6YC"
    affiliation: "Missouri University of Science and Technology"

doi: 10.5281/zenodo.10896042
date-released: 2024-01-05
url: "https://doi.org/10.5281/zenodo.10896042"
repository-code: "https://github.com/AP6YC/DeepART"
identifiers:
  - description: "The DOI of the latest DeepART Zenodo archive."
    type: "doi"
    value: "10.5281/zenodo.10896042"

GitHub Events

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Dependencies

.github/workflows/onefetcher.yml actions
  • actions/checkout v2 composite
old/l2m-requirements.txt pypi
  • l2logger *
  • l2metrics *
requirements.txt pypi
  • jupyterlab *
  • l2logger *
  • l2metrics *
  • matplotlib *
  • numpy *
  • pandas *
  • scikit-learn *
  • tensorflow *
src/l2m/pyproject.toml pypi
  • l2logger *
  • l2metrics *