A graph|based interpretability method for deep neural networks

Model interpretability - Azure Machine Learning | Microsoft Learn

Integrated Gradients is a popular explanation method for deep neural networks ... XRAI is a novel region-based saliency method based on Integrated ...

A detailed study of interpretability of deep neural network based top ...

We review a subset of existing top tagger models and explore different quantitative methods to identify which features play the most important ...

Improving Interpretability of Deep Neural Networks With Semantic ...

We also present a human-in-the-loop learning procedure, through which users can easily revise false predictions and the model based on the good interpretation ...

Interpretable and Generalizable Graph Learning via Stochastic ...

Explainability techniques for graph convolutional networks. In International Con- ference on Machine Learning Workshops, 2019 Workshop on Learning and ...

Shortcomings of Interpretability Taxonomies for Deep Neural Networks

(2020) [12] distinguish between local and global methods, gradient-based and perturbation-based methods on the other (methodology), and ...

INTERPRETABLE GRAPH NEURAL NETWORKS FOR TABULAR ...

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

An Interpretable Denoising Layer for Neural Networks Based on ...

Instead of the raw signal, the data processed by signal analysis techniques, such as the wavelet transform, are applied to fit the deep neural ...

An Interpretable Deep Learning Method for Identifying Extreme ...

In this study, a deep learning-based method with visual interpretability is proposed to identify seismic data under sensor faults interference.

Improving explainability of deep neural network-based ...

Improving explainability of deep neural network-based electrocardiogram interpretation ... A benchmark for interpretability methods in deep neural networks . In ...

Interpretability Methods in Machine Learning: A Brief Survey

For this reason, such models are sometimes called “black boxes.” Source: Neural Networks and Deep Learning, by Michael Nielsen. neuralnetworksanddeeplearning.

Logic Explained Deep Neural Networks: A General Approach to ...

... neural architectures via interpretable deep learning models called Logic Explained Networks (LENs). ... Compared to other concept-based techniques ...

Neural Network Interpretability — A review | by Antoine Hue

Activation maps are the first interpretation methods based on ... Interpretability of deep neural networks is a very vast field of ...

Convolutional Neural Network Tutorial | CNN 2025 - Simplilearn.com

This lack of interpretability can limit the trust placed in CNN-based systems, especially in critical applications like healthcare.

MedAI #34: Optimizing for Interpretability in Deep Neural Networks

Title: Optimizing for Interpretability in Deep Neural Networks Speaker: Mike Wu Abstract: Deep models have advanced prediction in many ...

Phys. Rev. Lett. 120, 145301 (2018) - Crystal Graph Convolutional ...

The use of machine learning methods ... Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material ...

Distill — Latest articles about machine learning

Understanding the building blocks and design choices of graph neural networks. ... Interpretability techniques are normally studied in isolation. We explore ...

Interpreting Deep Neural Networks - Institute for Advanced Study

machine learning models (both post-hoc and model-based methods can ... D vs P for model-based interpretability. Descriptive accuracy.

Explainable artificial intelligence - Wikipedia

Explainable AI (XAI), often overlapping with interpretable AI, or explainable machine learning (XML), either refers to an artificial intelligence (AI) ...

Evaluating Attribution for Graph Neural Networks - NIPS

Interpretability of machine learning models is critical to scientific understanding,. AI safety, and debugging. Attribution is one approach to ...

Benjamin Sanchez-Lengeling, Gentle Introduction to graph Neural ...

And once that is done, what are the suitable machine learning techniques for modeling molecules? ... Deep learning on graphs: successes, ...