Interpretable A|posteriori error indication for graph neural network ...

Interpretable A-posteriori Error Indication for Graph Neural Network ...

Given a black-box baseline GNN model, the end result is an interpretable GNN model that isolates regions in physical space, corresponding to sub ...

Interpretable A-posteriori error indication for graph neural network ...

Additionally, through a regularization procedure, the interpretable GNNs can also be used to identify, during inference, graph nodes that correspond to a ...

Interpretable A-posteriori Error Indication for Graph Neural Network ...

Additionally, through a regularization procedure, the interpretable GNNs can also be used to identify, during inference, graph nodes that ...

Shivam Barwey on LinkedIn: Interpretable A-posteriori Error ...

Our recent work on error indication for SciML models is published in CMAME! In this work, we enhance graph neural network surrogate models ...

Interpretable Fine-Tuning and Error Indication for Graph Neural ...

Data-driven surrogate modeling has surged in capability in recent years with the emergence of graph neural networks (GNNs), ...

Interpretable neural networks: principles and applications - Frontiers

The third approach is an interpretable graph neural network (GNN) that combines semantic graphs and DNNs, and its main idea is to utilize the semantic ...

Towards Interpretable Graph Neural Networks - ACM Digital Library

Extensive experiments conducted on both synthetic and real-world benchmarks demonstrate that the proposed GIP yields significantly superior ...

GNNExplainer: Generating Explanations for Graph Neural Networks

GNNEXPLAINER provides a variety of benefits, from the ability to visualize semantically relevant structures to interpretability, to giving insights into errors ...

Interpretable neural networks: principles and applications - PMC

Keywords: model decomposition, semantic graph, interpretable neural networks, electromagnetic neural network, interpretability ... errors of the network (Li et al ...

Interpretable Neural Networks for Graph Signal Denoising

For denoising a single smooth graph signal, the normalized mean square error of the proposed networks is around. 40% and 60% lower than that of graph Laplacian ...

Efficient and interpretable graph network representation for angle ...

Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and ...

Interpretable Neural Networks for Graph Signal Denoising

We propose an interpretable graph neural network framework to denoise single or multiple noisy graph signals. The proposed graph unrolling networks expand ...

How Interpretable Are Interpretable Graph Neural Networks?

An XGNN based on linear GNN with k >. 1 cannot satisfy Eq. 9, thus cannot approximate SubMT. When given more complicated GNNs, the approximation error to SubMT ...

GNNExplainer: Generating Explanations for Graph Neural Networks

GNNEXPLAINER provides a variety of benefits, from the ability to visualize semantically relevant structures to interpretability, to giving insights into errors ...

Interpretability in Graph Neural Networks

In graph analysis, motivated by the effectiveness of deep learning, graph neural networks (GNNs) are becoming increasingly popular in modeling graph data.

Explaining Graph Neural Networks for Vulnerability Discovery

... learning-based methods working directly on source code. Unfortunately, these neural networks are uninterpretable models, whose decision process is ...

What is the error histogram in neural network matlab? - MathWorks

Bins are the number of vertical bars you are observing on the graph. The total error range is divided into 30 smaller bins here. Y-axis ...

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis

For these two-stage methods, if the results from the first stage are not reliable, significant errors can be induced in the second stage. The past few years ...

Interpretable and Convergent Graph Neural Network Layers at Scale

Unfolded GNNs are a class of GNN methods derived from an interpretable optimization objective (aka energy function), which may face scalability ...

An interpretable graph convolutional neural network based fault ...

This study proposed a fault diagnosis method based on interpretable graph neural network (GNN) suitable for building energy systems.