Explainable Deep Learning: A Field Guide for the Uninitiated
A field guide to deep learning explainability for those uninitiated in the field.
Discusses the traits of a deep learning system that researchers enhance in explainability research.
Places explainability in the context of other related deep learning research areas
Introduces three simple dimensions defining the space of foundational methods that contribute to explainable deep learning.
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Associate Professor of Computer Science, Wright State University, Dayton OH
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A Framework for Explaining Predictions of NLP Models
Identification of contributing features towards the rupture risk prediction of intracranial aneurysms using LIME explainer
Visualization toolkit for neural networks in PyTorch
G-MARC : GUI for Model Agnostic IML on Rupture risk Classifier
Study and Visualization of Model Agnostic Interpretable ML Approaches on the classification of Rupture status of Intracranial Aneurysms
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