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dianna-exploration

This repository contains the expliratory and research work from the DIANNA project

https://github.com/dianna-ai/dianna-exploration

Science Score: 44.0%

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  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
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    DOI references
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    Academic publication links
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (4.0%) to scientific vocabulary

Keywords

explainable-artificial-intelligence neural-networks
Last synced: 6 months ago · JSON representation ·

Repository

This repository contains the expliratory and research work from the DIANNA project

Basic Info
Statistics
  • Stars: 2
  • Watchers: 5
  • Forks: 1
  • Open Issues: 21
  • Releases: 1
Topics
explainable-artificial-intelligence neural-networks
Created almost 5 years ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

Deep Insight AND Neural Network Analysis (DIANNA)

eXplainable AI (XAI) methods made usable by scientists

Repository for the exploration work.

In order to install the required packages, use the following command:

pip install -r requirements.in

All of the packages that will be installed, also through recursive install can be viewed in requirements.txt.

Owner

  • Name: Deep Insight And Neural Network Analysis (DIANNA)​
  • Login: dianna-ai
  • Kind: organization
  • Location: Amsterdam

Netherlands eScience Center and SURF project to build software for post-hoc explainability of deep neural networks for scientists​

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: dianna exploration
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Elena
    family-names: Ranguelova
    email: E.Ranguelova@esciencecenter.nl
    affiliation: Netherlands eScinece center
    orcid: 'https://orcid.org/0000-0002-9834-1756'
  - given-names: Yang
    family-names: Liu
    orcid: 'https://orcid.org/0000-0002-1966-8460'
  - given-names: Christiaan
    family-names: Meijer
    orcid: 'https://orcid.org/0000-0002-5529-5761'
  - given-names: Leon
    family-names: Oostrum
    orcid: 'https://orcid.org/0000-0001-8724-8372'
  - given-names: Willem
    family-names: van der Spek
identifiers:
  - type: doi
    value: 10.5281/zenodo.7985775
    description: Inital release
repository-code: 'https://github.com/dianna-ai/dianna-exploration'
abstract: |-
  eXplainable AI (XAI) methods made usable by scientists

  Repository for exploration work.
keywords:
  - Explainable AI (XAI)
license: Apache-2.0
commit: >-
  https://github.com/dianna-ai/dianna-exploration/commit/54612de5efe89948bc0f0c8a91b711cc055deb71
version: Initial
date-released: '2023-05-30'

GitHub Events

Total
  • Issues event: 6
  • Issue comment event: 2
Last Year
  • Issues event: 6
  • Issue comment event: 2

Issues and Pull Requests

Last synced: 6 months ago

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  • Total issues: 3
  • Total pull requests: 0
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  • Total issue authors: 1
  • Total pull request authors: 0
  • Average comments per issue: 4.67
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
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  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
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Top Authors
Issue Authors
  • elboyran (4)
  • ClaireDons (1)
Pull Request Authors
  • cwmeijer (1)
Top Labels
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Dependencies

requirements.txt pypi
  • captum ==0.4.0
  • dianna ==0.1.0
  • keras ==2.4.0
  • lime ==0.2.0.0
  • onnx ==1.9.0
  • onnx-tf ==1.1.2
  • onnxruntime ==1.7.0
  • pandas ==1.3.4
  • scikit-learn ==1.0.1
  • scipy ==1.6.2
  • seaborn ==0.11.2
  • shap ==0.39.0
  • tensorflow ==2.4.1
  • torch ==1.8.1
  • torchtext ==0.9.1
  • torchvision ==0.9.1
  • tqdm ==4.62.3
requirements.in pypi
  • captum >=0.4.0
  • onnx2keras >=0.0.24
  • quantus >=0.4.0
  • seaborn >=0.12.2
  • spacy >=3.5.2
  • torch ==1.9.0
  • torchtext ==0.10.0
  • torchvision ==0.10.0
  • wandb >=0.15.2