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Improving interpretability via regularization of neural activation sensitivity
State-of-the-art deep neural networks (DNNs) are highly effective at tackling many real-world tasks. However, their widespread adoption in...
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Reliability and Interpretability in Science and Deep Learning
In recent years, the question of the reliability of Machine Learning (ML) methods has acquired significant importance, and the analysis of the...
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Approach to provide interpretability in machine learning models for image classification
One of the main reasons why machine learning (ML) methods are not yet widely used in productive business processes is the lack of confidence in the...
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Interpretability in Deep Learning
This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models,...
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Enhancing trust and interpretability of complex machine learning models using local interpretable model agnostic shap explanations
With the rapid advancement of artificial intelligence (AI) technology and analytics compute engines, machine learning (ML) models have become...
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Re-interpreting rules interpretability
Trustworthy machine learning requires a high level of interpretability of machine learning models, yet many models are inherently black-boxes....
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A process for improving the quality and interpretability of data visualizations
Nowadays, constructing and interpreting data visualizations has become essential for simplifying access to information, improving data...
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Interpretability-Based Cross-Silo Federated Learning
The severe challenge encountered in cross-silo federated learning (FL) is the performance degradation caused by data heterogeneity. To overcome it,...
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Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond
Deep neural networks have been well-known for their superb handling of various machine learning and artificial intelligence tasks. However, due to...
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A survey on the interpretability of deep learning in medical diagnosis
Deep learning has demonstrated remarkable performance in the medical domain, with accuracy that rivals or even exceeds that of human experts....
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VolPAM: Volumetric Phenotype-Activation-Map for data-driven discovery of 3D imaging phenotypes and interpretability
Knowledge about the subtypes of a disease critically affects clinical decisions ranging from the choice of therapeutic options to patient management....
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Unraveling the intricacies of EEG seizure detection: A comprehensive exploration of machine learning model performance, interpretability, and clinical insights
In neurology, it is critical to promptly and precisely identify epileptic episodes using EEG data. Interpretability and thorough model evaluation are...
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Distinguishing between Crohn’s disease and ulcerative colitis using deep learning models with interpretability
Crohn’s disease and ulcerative colitis are two chronic diseases that cause inflammation in the tissues of the entire gastrointestinal tract and are...
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Interpretability-Mask: a label-preserving data augmentation scheme for better classification
Data augmentation effectively alleviates the over-fitting problem in convolutional neural network-based (CNN-based) models, especially in the limited...
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A hybrid transformer with domain adaptation using interpretability techniques for the application to the detection of risk situations
Multimedia approaches are strongly required in multi-modal data processing for the detection and recognition of specific events in the data. Hybrid...
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Style spectroscope: improve interpretability and controllability through Fourier analysis
Universal style transfer (UST) infuses styles from arbitrary reference images into content images. Existing methods, while enjoying many practical...
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Evaluating Self-attention Interpretability Through Human-Grounded Experimental Protocol
Attention mechanisms have played a crucial role in the development of complex architectures such as Transformers in natural language processing....
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Introduction to Interpretability
Artificial Intelligence (AI) and modern computing captivate a large and growing number of people. It’s fascinating to see how they progressed from a...
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Measures of Interpretability
This chapter will act as the introduction to the technical discussions in the book. We will start by establishing some of the basic notations that we...
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Fairness-Aware Mixture of Experts with Interpretability Budgets
As artificial intelligence becomes more pervasive, explainability and the need to interpret machine learning models’ behavior emerge as critical...