A graph|based interpretability method for deep neural networks

A graph-based interpretability method for deep neural networks

In this paper, we propose a graph-based interpretability method for deep neural networks (GIMDNN). The running parameters of DNNs are modeled as a graph.

A graph-based interpretability method for deep neural networks

AbstractWith the development of artificial intelligence, the most representative deep learning has been applied to various fields, which is greatly ...

Interpretability Methods for Graph Neural Networks - IEEE Xplore

The emerging graph neural network models (GNNs) have demonstrated great potential and success for downstream graph machine learning tasks, such as graph and ...

A graph-based interpretability method for deep neural networks

Semantic Scholar extracted view of "A graph-based interpretability method for deep neural networks" by Tao Wang et al.

A graph-based interpretability method for deep neural networks

Interpretability improvement of RBF-based neurofuzzy systems using regularized learning ... Radial-basis-function (RBF) networks are mathematically equivalent to ...

A graph-based interpretability method for deep neural networks - OUCI

A graph-based interpretability method for deep neural networks · List of references · Publications that cite this publication. Online Learning Behavior Analysis ...

[2306.01958] A Survey on Explainability of Graph Neural Networks

... methods, identifying gaps, and fostering further advancements in interpretable graph-based machine learning. Comments: submitted to Bulletin ...

Reliable interpretability of biology-inspired deep neural networks

Our study reveals the broad importance of methods to ensure robust and bias-aware interpretability in biology-inspired deep learning.

A Demonstration of Interpretability Methods for Graph Neural Networks

In this work, we focus on interpretability of deep learning meth- ods over graph-structured data. Graph neural networks (GNNs) [22] are useful in graphs and ...

GNNBook@2023: Interpretability in Graph Neural Networks

Interpretable machine learning, or explainable artificial intelligence, is experiencing rapid developments to tackle the opacity issue of deep learning ...

GNNExplainer: Generating Explanations for Graph Neural Networks

At a high level, we can group those interpretability methods for non-graph neural networks into two main families. ... Interpretable Graph Convolutional Neural ...

Interpretability Methods for Graph Neural Networks

GNNs are a type of deep learning models designed to tackle graph-related tasks in an end-to-end manner. Therefore, it remains a desirable yet nontrivial task to ...

A Semantic Interpretation Method for Deep Neural Networks Based ...

In order to use semantic information which is more understandable and close to human thought to interpret the deep neural network and increase ...

A Demonstration of Interpretability Methods for Graph Neural Networks

This paper demonstrates gInterpreter with an interactive performance profiling of 15 recent GNN inter-pretability methods, aiming to explain the complex deep ...

Interpretability in Graph Neural Networks

pretation, the methods of obtaining interpretation in traditional deep models, the opportunities and challenges to achieve interpretability in GNN models.

[2308.08945] Interpretable Graph Neural Networks for Tabular Data

... deep neural networks, precluding users from following the logic behind the model predictions. We propose an approach, called IGNNet ...

Explaining Graph Neural Networks Using Interpretable Local ...

Perturbation-based explainability methods. Perturbation-based methods are widely used to ex- plain deep image and graph models (Yuan et al., 2022). The.

flyingdoog/awesome-graph-explainability-papers - GitHub

[GRADES & NDA'23] A Demonstration of Interpretability Methods for Graph Neural Networks [paper]; [Arxiv 23] Self-Explainable Graph Neural Networks for Link ...

A Benchmark for Interpretability Methods in Deep Neural Networks

To the best of our knowledge, unlike prior modification based evaluation measures, our benchmark requires retraining the model from random initialization on the ...

A Benchmark for Interpretability Methods in Deep Neural Networks

... methods produce estimates of feature importance that are not better than a random designation of feature importance. Only certain ensemble based approaches ...