Neural network software is experiencing rapid growth due to the increasing demand for autonomous vehicles and Advanced Driver Assistance Systems. These systems rely on…
Browsing: Interpretability
SQUID is a genomic DNN interpretability framework that utilizes domain-specific surrogate modelling to improve the understanding of underlying biological mechanisms in deep neural networks.…
This article discusses the use of Shapley Additive exPlanations (SHAP), a game-theoretic technique, to enhance the interpretability of machine learning models in healthcare. The…
This article discusses the importance of interpretability in machine learning, particularly in high-stakes applications such as healthcare. The authors propose a new method that…
Machine learning has revolutionized various domains and is continuously pushing the boundaries of what is possible in artificial intelligence. However, one of the major…
This article discusses the recent advancements in deep learning and its impact on various industries and domains. It highlights the success of deep learning…
Explainability is a crucial concept in the regulation of artificial intelligence, as it allows for transparency and oversight of complex models. It is seen…
This article discusses the use of advanced signal processing methods and deep neural networks for machinery fault diagnosis. These techniques can extract fault features…
This article discusses the development of a hybrid physics-ML model for predicting the rate of penetration (ROP) in the Halahatang oil field. The model…
This article provides a step-by-step guide for developing a model explainability tool for machine learning models. It emphasizes the importance of transparency and interpretability…