Advanced Electronics Technologies for AI Research Report 2026-2036: Global Convergence of Quantum and Neuromorphic Computing Powers $2 Trillion Market Opportunity

Traditional silicon-based architectures face challenges from rising AI demands, leading to a shift towards specialized computing solutions. Neuromorphic computing reduces power usage while enhancing ...
IEEE

Graph Neural Networks for Next-Generation-IoT: Recent Advances and Open Challenges

Abstract: Graph Neural Networks (GNNs) have emerged as a powerful framework for modeling complex interconnected systems, hence making them particularly well-suited to address the growing challenges of ...
C&EN

Multilevel Fusion Graph Neural Network for Molecule Property Prediction

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.5c01525. Efficiency analysis of different normalization strategies ...
C&EN

The Dynamic Connection Layer (DCL): Enhancing Topological Representation in Chemical Graph Neural Networks

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Non-Commercial (NC): Only non-commercial uses of the work are permitted. In ...
Scientific Research Publishing

Jin, W., Barzilay, R. and Jaakkola, T. (2018) Junction Tree Variational Autoencoder for Molecular Graph Generation. International Conference on Machine Learning, Stockholm, 10 ...

ABSTRACT: Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to ...
Scientific Research Publishing

Wang, X. and Shen, H.W. (2022) Gnninterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks. arXiv: 2209.07924.

ABSTRACT: Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to ...
Frontiers

Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration

Accurate molecular subtypes prediction of cancer patients is significant for personalized cancer diagnosis and treatments. Large amount of multi-omics data and the advancement of data-driven methods ...
Microsoft

HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations

Recently, various response generation models for two-party conversations have achieved impressive improvements, but less effort has been paid to multi-party conversations (MPCs) which are more ...