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)
L2X - Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation.
L2X Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation at ICML 2018,
113 Sep 06, 2022
An Empirical Review of Optimization Techniques for Quantum Variational Circuits
QVC Optimizer Review Code for the paper "An Empirical Review of Optimization Techniques for Quantum Variational Circuits". Each of the python files ca
5 Jun 28, 2022
Summary Explorer is a tool to visually explore the state-of-the-art in text summarization.
Summary Explorer is a tool to visually explore the state-of-the-art in text summarization.
42 Aug 14, 2022
Code for "High-Precision Model-Agnostic Explanations" paper
Anchor This repository has code for the paper High-Precision Model-Agnostic Explanations. An anchor explanation is a rule that sufficiently “anchors”
735 Jan 05, 2023
Visualization Toolbox for Long Short Term Memory networks (LSTMs)
Visualization Toolbox for Long Short Term Memory networks (LSTMs)
1.1k Jan 04, 2023
Tool for visualizing attention in the Transformer model (BERT, GPT-2, Albert, XLNet, RoBERTa, CTRL, etc.)
Tool for visualizing attention in the Transformer model (BERT, GPT-2, Albert, XLNet, RoBERTa, CTRL, etc.)
4.7k Jan 01, 2023
An intuitive library to add plotting functionality to scikit-learn objects.
Welcome to Scikit-plot Single line functions for detailed visualizations The quickest and easiest way to go from analysis... ...to this. Scikit-plot i
2.3k Dec 31, 2022
JittorVis - Visual understanding of deep learning model.
JittorVis - Visual understanding of deep learning model.
182 Jan 06, 2023
Visualize a molecule and its conformations in Jupyter notebooks/lab using py3dmol
Mol Viewer This is a simple package wrapping py3dmol for a single command visualization of a RDKit molecule and its conformations (embed as Conformer
1 Feb 11, 2022
Pytorch Feature Map Extractor
MapExtrackt Convolutional Neural Networks Are Beautiful We all take our eyes for granted, we glance at an object for an instant and our brains can ide
40 Dec 07, 2022
ModelChimp is an experiment tracker for Deep Learning and Machine Learning experiments.
ModelChimp What is ModelChimp? ModelChimp is an experiment tracker for Deep Learning and Machine Learning experiments. ModelChimp provides the followi
124 Dec 21, 2022
Logging MXNet data for visualization in TensorBoard.
Logging MXNet Data for Visualization in TensorBoard Overview MXBoard provides a set of APIs for logging MXNet data for visualization in TensorBoard. T
327 Dec 05, 2022
A library that implements fairness-aware machine learning algorithms
Themis ML themis-ml is a Python library built on top of pandas and sklearnthat implements fairness-aware machine learning algorithms. Fairness-aware M
105 Dec 30, 2022
pytorch implementation of "Distilling a Neural Network Into a Soft Decision Tree"
Soft-Decision-Tree Soft-Decision-Tree is the pytorch implementation of Distilling a Neural Network Into a Soft Decision Tree, paper recently published
262 Dec 04, 2022
A ultra-lightweight 3D renderer of the Tensorflow/Keras neural network architectures
A ultra-lightweight 3D renderer of the Tensorflow/Keras neural network architectures
16 Nov 17, 2021
A collection of research papers and software related to explainability in graph machine learning.
A collection of research papers and software related to explainability in graph machine learning.
1.9k Dec 26, 2022
A data-driven approach to quantify the value of classifiers in a machine learning ensemble.
Documentation | External Resources | Research Paper Shapley is a Python library for evaluating binary classifiers in a machine learning ensemble. The
187 Dec 27, 2022
A python library for decision tree visualization and model interpretation.
dtreeviz : Decision Tree Visualization Description A python library for decision tree visualization and model interpretation. Currently supports sciki
2.4k Jan 02, 2023
Interpretability and explainability of data and machine learning models
AI Explainability 360 (v0.2.1) The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datase
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Interactive convnet features visualization for Keras
Quiver Interactive convnet features visualization for Keras The quiver workflow Video Demo Build your model in keras model = Model(...) Launch the vis
1.7k Dec 21, 2022