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Exploring Multi-Task Learning for Explainability
Machine Learning (ML) model understanding and interpretation is an essential component of several applications in different domains. Several...
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ConveXplainer for Graph Neural Networks
Graph neural networks (GNNs) have become the most prominent framework for representation learning on graph-structured data. Nonetheless, cue to its...
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Generating Explanations for Conceptual Validation of Graph Neural Networks: An Investigation of Symbolic Predicates Learned on Relevance-Ranked Sub-Graphs
Graph Neural Networks (GNN) show good performance in relational data classification. However, their contribution to concept learning and the...
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Hybrid LSTM-Graph Convolutional Neural Network with Wavelet Transform and Correlation Analysis for Electrical Demand Forecasting
Accurate electrical demand forecasting is essential for power system efficiency, renewable energy investment, and cost-effective electricity...
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An Integrated Intelligent System for Breast Cancer Detection at Early Stages Using IR Images and Machine Learning Methods with Explainability
Breast cancer is the second most common cause of death among women. An early diagnosis is vital for reducing the fatality rate in the fight against...
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Concept-Oriented Self-Explaining Neural Networks
Recent works on deriving interpretability of machine learning models have focused on post-hoc explanations, and it is believed that there is a...
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Towards Nonparametric Topological Layers in Neural Networks
Various topological techniques and tools have been applied to neural networks in terms of network complexity, explainability, and performance. One...
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Interpretability in Graph Neural Networks
Interpretable machine learning, or explainable artificial intelligence, is experiencing rapid developments to tackle the opacity issue of deep...
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Towards rigorous understanding of neural networks via semantics-preserving transformations
In this paper, we present an algebraic approach to the precise and global verification and explanation of Rectifier Neural Networks , a subclass of Piec...
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EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks
Ejection fraction (EF) is a key indicator of cardiac function, allowing identification of patients prone to heart dysfunctions such as heart failure....
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Graph Neural Networks in Natural Language Processing
Natural language processing (NLP) and understanding aim to read from unformatted text to accomplish different tasks. While word embeddings learned by...
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Explainability
The field of explainable artificial intelligence (XAI), or interpretable AI, or sometimes explainable Machine Learning is a research field into...
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CoSP: co-selection pick for a global explainability of black box machine learning models
Recently, few methods for understanding machine learning model’s outputs have been developed. SHAP and LIME are two well-known examples of these...
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Pruning Deep Neural Networks for Green Energy-Efficient Models: A Survey
Over the past few years, larger and deeper neural network models, particularly convolutional neural networks (CNNs), have consistently advanced...
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Automated Code Discovery via Graph Neural Networks and Generative AI
Quantitative Ethnographic researchers sometimes use machine learning to help them discover codes in discourse. Commonly used techniques, such as...
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Intention-aware denoising graph neural network for session-based recommendation
Session-based recommendation anticipates the next potential interest of users based on their previous anonymous interactions, which is a crucial and...
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Fair and Privacy-Preserving Graph Neural Network
Graph neural networks (GNNs) have demonstrated superior performance in modeling graph-structured. They are vastly applied in various high-stakes...
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Graph Neural Networks in Program Analysis
Program analysis aims to determine if a program’s behavior complies with some specification. Commonly, program analyses need to be defined and tuned...
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LNN: Logical Neural Networks
Logical Neural Networks (LNN) is a framework that assumes knowledge of a logic program a-priori and uses gradient descent to fit the logic program to...
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An Efficient Approach Based on Graph Neural Networks for Predicting Wait Time in Job Schedulers
The objective of this study is to predict the wait time in job schedulers with high accuracy. Job executions in supercomputers or data centers are...