A comprehensive review of deep neural network interpretation using topological data analysis
B Zhang, Z He, H Lin - Neurocomputing, 2024 - Elsevier
Deep neural networks have achieved significant success across various fields, but their
intrinsic black-box nature hinders the further development. Addressing the interpretability …
intrinsic black-box nature hinders the further development. Addressing the interpretability …
A functional contextual account of background knowledge in categorization: Implications for artificial general intelligence and cognitive accounts of general knowledge
Psychology has benefited from an enormous wealth of knowledge about processes of
cognition in relation to how the brain organizes information. Within the categorization …
cognition in relation to how the brain organizes information. Within the categorization …
A novel fuzzy knowledge graph pairs approach in decision making
Abstract Fuzzy Knowledge Graph (FKG) has recently been emerging as one of the key
techniques for supporting classification and decision-making problems. FKG is a novel …
techniques for supporting classification and decision-making problems. FKG is a novel …
Knowledge correlation graph-guided multi-source interaction domain adaptation network for rotating machinery fault diagnosis
Z Wu, H Jiang, X Wang, H Zhu - ISA transactions, 2023 - Elsevier
Leveraging generalized knowledge from multiple source domains with rich labels to the
target domain without labeled data is a more realistic and challenging issue compared with …
target domain without labeled data is a more realistic and challenging issue compared with …
Functional network: A novel framework for interpretability of deep neural networks
B Zhang, Z Dong, J Zhang, H Lin - Neurocomputing, 2023 - Elsevier
The layered structure of deep neural networks hinders the use of numerous analysis tools
and thus the development of its interpretability. Inspired by the success of functional brain …
and thus the development of its interpretability. Inspired by the success of functional brain …
Boosting short term electric load forecasting of high & medium voltage substations with visibility graphs and graph neural networks
Modern power grids are faced with a series of challenges, such as the ever-increasing
demand for renewable energy sources, extensive urbanization, climate and energy crisis …
demand for renewable energy sources, extensive urbanization, climate and energy crisis …
Functional loops: Monitoring functional organization of deep neural networks using algebraic topology
B Zhang, H Lin - Neural Networks, 2024 - Elsevier
Various topological methods have emerged in recent years to investigate the inner workings
of deep neural networks (DNNs) based on the structural and weight information. However …
of deep neural networks (DNNs) based on the structural and weight information. However …
[HTML][HTML] Effective graph-neural-network based models for discovering Structural Hole Spanners in large-scale and diverse networks
Abstract A Structural Hole Spanner (SHS) is a set of nodes in a network that act as a bridge
among different otherwise disconnected communities. While several solutions exist for SHS …
among different otherwise disconnected communities. While several solutions exist for SHS …
Enhancing Network Resilience through Machine Learning-powered Graph Combinatorial Optimization: Applications in Cyber Defense and Information Diffusion
D Goel - arxiv preprint arxiv:2310.10667, 2023 - arxiv.org
With the burgeoning advancements of computing and network communication technologies,
network infrastructures and their application environments have become increasingly …
network infrastructures and their application environments have become increasingly …
A neuro-symbolic approach to enhance interpretability of graph neural network through the integration of external knowledge
K Raj - Proceedings of the 32nd ACM International …, 2023 - dl.acm.org
Graph Neural Networks (GNNs) have shown remarkable performance in tackling complex
tasks. However, interpreting the decision-making process of GNNs remains a challenge. To …
tasks. However, interpreting the decision-making process of GNNs remains a challenge. To …