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Explaining, Evaluating and Enhancing Neural Networks’ Learned Representations
Most efforts in interpretability in deep learning have focused on (1) extracting explanations of a specific downstream task in relation to the input...
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XRL-SHAP-Cache: an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks
Content delivery networks (CDNs) play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse...
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A Graph Neural Network Approach for Evaluating Correctness of Groups of Duplicates
Unlabeled entity deduplication is a relevant task already studied in the recent literature. Most methods can be traced back to the following...
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Knowledge-aware attentional neural network for review-based movie recommendation with explanations
In this paper, we propose a knowledge-aware attentional neural network (KANN) for dealing with movie recommendation tasks by extracting knowledge...
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Model Explainability and Interpretability
In this book, we will begin with an introduction to model explainability and interpretability basics, ethical considerations in AI applications, and...
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Class imbalance in multi-resident activity recognition: an evaluative study on explainability of deep learning approaches
Recognizing multiple residents’ activities is a pivotal domain within active and assisted living technologies, where the diversity of actions in a...
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An evolving graph convolutional network for dynamic functional brain network
Brain networks have received extensive attention because of its important significance in understanding human brain organization and analyzing...
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Polynomial dendritic neural networks
Although many artificial neural networks have achieved success in practical applications, there is still a concern among many over their “black box”...
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Transparency and Explainability
In the rapidly advancing landscape of artificial intelligence (AI), the decisions made by AI systems are playing an increasingly crucial role in...
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Temporal enhanced inductive graph knowledge tracing
AbstractThe COVID-19 pandemic has led to a surge in online education, yielding increased attention on knowledge tracing (KT). KT involves predicting...
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Are Graph Neural Network Explainers Robust to Graph Noises?
With the rapid deployment of graph neural networks (GNNs) based techniques in a wide range of applications such as link prediction, community...
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First International Workshop on Graph-Based Approaches in Information Retrieval (IRonGraphs 2024)
In the dynamic field of information retrieval, the adoption of graph-based approaches has become a notable research trend. Fueled by the growing...
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A walk in the black-box: 3D visualization of large neural networks in virtual reality
Within the last decade Deep Learning has become a tool for solving challenging problems like image recognition. Still, Convolutional Neural Networks...
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MEGAN: Multi-explanation Graph Attention Network
We propose a multi-explanation graph attention network (MEGAN). Unlike existing graph explainability methods, our network can produce node and edge...
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Mental stress detection from ultra-short heart rate variability using explainable graph convolutional network with network pruning and quantisation
This study introduces a novel pruning approach based on explainable graph convolutional networks, strategically amalgamating pruning and...
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Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work
The increasing prevalence of Artificial Intelligence (AI) in safety-critical contexts such as air-traffic control leads to systems that are practical...
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Explaining Deep Neural Networks for Point Clouds Using Gradient-Based Visualisations
Explaining decisions made by deep neural networks is a rapidly advancing research topic. In recent years, several approaches have attempted to...
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Enhancing e-commerce recommendations with a novel scale-aware spectral graph wavelets framework
As e-commerce thrives, robust recommendation systems that effectively cater to new users or items and manage large datasets are imperative. This...
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How Tasty Is This Dish? Studying User-Recipe Interactions with a Rating Prediction Algorithm and Graph Neural Networks
Food computing has gained significant attention in recent years due to its direct relation to our health, habits, and cultural traditions....
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Fusing Modalities by Multiplexed Graph Neural Networks for Outcome Prediction in Tuberculosis
In a complex disease such as tuberculosis, the evidence for the disease and its evolution may be present in multiple modalities such as clinical,...