A collection of research papers and software related to explainability in graph machine learning.

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Deep Learning Model Explanationdeep-learninggraphgraph-algorithmsawesome-listgraphmlexplainable-aiexplainable-ml
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  • Add new citation: Numeroso et al.

    Add new citation: Numeroso et al.

    Hi 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

    opened by danilonumeroso 1
  • added GCExplainer

    added GCExplainer

    You 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.

    opened by sbonner0 1
  • Added new references

    Added new references

    Two 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)
    opened by nomisto 0
  • Include one more paper from NeurIPS 2020

    Include one more paper from NeurIPS 2020

    The 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.

    opened by joaquincabezas 0
  • Overwhelming amount of papers

    Overwhelming amount of papers

    Hi, 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

    opened by joaquincabezas 1
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Data Science & AI: Unlocking new science insights
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GitHub Repository
A ultra-lightweight 3D renderer of the Tensorflow/Keras neural network architectures

A ultra-lightweight 3D renderer of the Tensorflow/Keras neural network architectures

Souvik Pratiher 16 Nov 17, 2021
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

Tzu-Wei Huang 7.5k Jan 07, 2023
⬛ Python Individual Conditional Expectation Plot Toolbox

⬛ PyCEbox Python Individual Conditional Expectation Plot Toolbox A Python implementation of individual conditional expecation plots inspired by R's IC

Austin Rochford 140 Dec 30, 2022
Python implementation of R package breakDown

pyBreakDown Python implementation of breakDown package (https://github.com/pbiecek/breakDown). Docs: https://pybreakdown.readthedocs.io. Requirements

MI^2 DataLab 41 Mar 17, 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

ModelChimp 124 Dec 21, 2022
Net2Vis automatically generates abstract visualizations for convolutional neural networks from Keras code.

Automatic neural network visualizations generated in your browser!

Visual Computing Group (Ulm University) 99 Nov 30, 2022
👋🦊 Xplique is a Python toolkit dedicated to explainability, currently based on Tensorflow.

👋🦊 Xplique is a Python toolkit dedicated to explainability, currently based on Tensorflow.

DEEL 343 Jan 02, 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.)

Jesse Vig 4.7k Jan 01, 2023
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,

Jianbo Chen 113 Sep 06, 2022
A game theoretic approach to explain the output of any machine learning model.

SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allo

Scott Lundberg 18.3k Jan 08, 2023
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

Kim Heecheol 262 Dec 04, 2022
TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, Korean, Chinese, German and Easy to adapt for other languages)

🤪 TensorFlowTTS provides real-time state-of-the-art speech synthesis architectures such as Tacotron-2, Melgan, Multiband-Melgan, FastSpeech, FastSpeech2 based-on TensorFlow 2. With Tensorflow 2, we c

3k Jan 04, 2023
Algorithms for monitoring and explaining machine learning models

Alibi is an open source Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-qual

Seldon 1.9k Dec 30, 2022
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

Alvin Wan 556 Dec 20, 2022
Visualization Toolbox for Long Short Term Memory networks (LSTMs)

Visualization Toolbox for Long Short Term Memory networks (LSTMs)

Hendrik Strobelt 1.1k Jan 04, 2023
python partial dependence plot toolbox

PDPbox python partial dependence plot toolbox Motivation This repository is inspired by ICEbox. The goal is to visualize the impact of certain feature

Li Jiangchun 722 Dec 30, 2022
Code for visualizing the loss landscape of neural nets

Visualizing the Loss Landscape of Neural Nets This repository contains the PyTorch code for the paper Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer

Tom Goldstein 2.2k Dec 30, 2022
Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

Class Activation Map methods implemented in Pytorch pip install grad-cam ⭐ Comprehensive collection of Pixel Attribution methods for Computer Vision.

Jacob Gildenblat 6.5k Jan 01, 2023
🎆 A visualization of the CapsNet layers to better understand how it works

CapsNet-Visualization For more information on capsule networks check out my Medium articles here and here. Setup Use pip to install the required pytho

Nick Bourdakos 387 Dec 06, 2022
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

Reiichiro Nakano 2.3k Dec 31, 2022