Deep Learning and Graph Neural Network Interpretable papers and codes.
Part One: Deep Learning Interpretable Survey
Title
Date
Links
First Author
Code
A Survey on Explainable Artificial Intelligence (XAI): towards Medical XAI
2020
CoRR Erico Tjoa
No
A Survey on Techniques, Applications and Security of Machine Learning Interpretability
2019
计算机研究与发展 纪守领
No
Visual Interpretability for Deep Learning: a Survey
2018
Frontiers of Information Technology & Electronic Engineering Quanshi Zhang
No
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018
IEEE Access Amina Adadi
No
Interpretable Machine Learning: The fuss, the concrete and the questions
2017
ICML Been Kim
No
Part Two: Deep Learning Interpretable Papers
Title
Date
Links
First Author
Model
SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation
2020
MICCAI Jesse Sun
U-Net
Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases
2019
sensors Iam Palatnik de Sousa
---
Uncertainty Modeling and Interpretability In Convolutional Neural Networks for Polyp Segmentation
2018
IEEE International Workshop on Machine Learning For Signal Processing Kristoffer Wickstrøm
No
Part Three: Graph Neural Network Interpretable Survey
Title
Date
Links
First Author
Code
Explainability Methods for Graph Convolutional Neural Networks
2019
CVPR Phillip E.Pope
github
Explainability Techniques for Graph Convolutional Networks
2019
ICML Federico Baldassarre
github
Part Four: Graph Neural Network Interpretable Papers
Title
Date
Links
First Author
Code
Interpretable Neuron Structuring with Graph Spectral Regularization
2020
IDA Alexander Tong
github
XGNN: Towards Model-Level Explanations of Graph Neural Networks
2020
KDD Hao Yuan
No
Interpretable and Efficient Heterogeneous Graph Convolutional Network
2020
CoRR Yaming Yang
Pytorch
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling
2020
CoRR Emmanuel Noutahi
No
GNNExplainer: Generating Explanations for Graph Neural Networks
2019
NeurIPS Rex Ying
github
Factor Graph Neural Network
2019
CoRR Zhen Zhang
github
Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks
2019
CoRR Philip E.Pope
No
BayesGrad: Explaining Predictions of Graph Convolutional Networks
2018
ICONIP Hirotaka Akita
github
Part Five: Deep Learning Interpretable Scholars Part Six: Technology Tools 1、Captum Convolutional Neural Network Visualizations
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