A professionally curated list of awesome resources (paper, code, data, etc.) on Deep Graph Anomaly Detection (DGAD), which is the first work to comprehensively and systematically summarize the recent ...

Bipolar disorder (BD) affects approximately 45 million individuals worldwide and is characterized by recurrent episodes of mania, hypomania, and depression, with an average diagnostic delay exceeding ...

Abstract: This paper addresses the problem of dual-technology scheduling in hybrid Internet-of-things (IoT) networks that integrate Optical Wireless Communication (OWC) alongside Radio Frequency (RF).

👉 Complete articles on Geometric Deep Learning, Graph Neural Networks, Topological Data Analysis with exercises are available on my Substack newsletter Hands-on Geometric Deep Learning The authors ...

F. Gama, A. G. Marques, G. Leus, and A. Ribeiro, "Convolutional Neural Network Architectures for Signals Supported on Graphs," IEEE Trans. Signal Process., vol. 67 ...

Given the wide range of configuration choices, setting up a Graph Neural Network can be daunting. Wouldn’t it be helpful to have a criterion that at least filters out less promising architectures?

Missing data remains a persistent and pervasive challenge across a wide range of domains, significantly impacting data analysis pipelines, predictive modeling outcomes, and the reliability of decision ...

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. To evaluate the defect formation energies, first-principles ...

The COVID-19 pandemic affected countries across the globe, demanding drastic public health policies to mitigate the spread of infection, which led to economic crises as a collateral damage. In this ...

Physics-based deep learning frameworks have shown to be effective in accurately modeling the dynamics of complex physical systems with generalization capability across problem inputs. However, ...