Awesome Explainable Graph Reasoning
A collection of research papers and software related to explainability in graph machine learning.
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A collection of research papers and software related to explainability in graph machine learning.
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Add new citation: Numeroso et al.
opened by danilonumeroso 1Hi all, I've added a new reference to a paper of mine related to counterfactual explanations for molecule predictions. I hope this is appreciated :)
Link to paper: https://arxiv.org/abs/2104.08060
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added GCExplainer
opened by sbonner0 1You might want to double check this commit is ok - I added a new sub-heading called concept based methods which was not covered by the survey paper the rest of the approaches are categorised into.
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Added new references
opened by nomisto 0Two papers on rule-based reasoning:
- AnyBURL (Meilicke et. al)
- SAFRAN (Ott et. al)
And one application note on a web application for visualizing predictions and their explanations using made my the approaches above:
- LinkExplorer (Ott et. al)
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Include one more paper from NeurIPS 2020
opened by joaquincabezas 0The work 'Evaluating Attribution for Graph Neural Networks' is particularly useful because of its approach as a benchmarking. It comprises several attribution techniques and GNN architectures.
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Overwhelming amount of papers
opened by joaquincabezas 1Hi, I have been impressed about how fast is this field growing. As I continue reading and learning, I will contribute with papers to make this list even better.
In particular, @flyingdoog is maintaining a list with the papers (grouped by year) at https://github.com/flyingdoog/awesome-graph-explainability-papers that can be interesting to review
Releases(v_00001)
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v_00001(Dec 2, 2021)
Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet
Neural-Backed Decision Trees · Site · Paper · Blog · Video Alvin Wan, *Lisa Dunlap, *Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah
556 Dec 20, 2022
Delve is a Python package for analyzing the inference dynamics of your PyTorch model.
Delve is a Python package for analyzing the inference dynamics of your PyTorch model.
73 Dec 12, 2022
A Practical Debugging Tool for Training Deep Neural Networks
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31 Aug 14, 2022
Net2Vis automatically generates abstract visualizations for convolutional neural networks from Keras code.
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99 Nov 30, 2022
Visual analysis and diagnostic tools to facilitate machine learning model selection.
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Code for "High-Precision Model-Agnostic Explanations" paper
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182 Jan 06, 2023
tensorboard for pytorch (and chainer, mxnet, numpy, ...)
tensorboardX Write TensorBoard events with simple function call. The current release (v2.1) is tested on anaconda3, with PyTorch 1.5.1 / torchvision 0
7.5k Jan 07, 2023
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56 Jan 03, 2023
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A collection of research papers and software related to explainability in graph machine learning.
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Pytorch implementation of convolutional neural network visualization techniques
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Visualizer for neural network, deep learning, and machine learning models
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A library that implements fairness-aware machine learning algorithms
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Algorithms for monitoring and explaining machine learning models
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Visualization toolkit for neural networks in PyTorch! Demo -->
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Auralisation of learned features in CNN (for audio)
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Lucid library adapted for PyTorch
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