IEEE

Learning Dynamic Graph Embeddings with Neural Controlled Differential Equations

Abstract: This paper focuses on representation learning for dynamic graphs with temporal interactions. A fundamental issue is that both the graph structure and the nodes own their own dynamics, and ...
C&EN

ML-PLA: Enhancing Protein–Ligand Binding Affinity Prediction with Microenvironment and Long-Range Interaction-Aware Graph Neural Networks

School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, Hubei 430200, China ...
Frontiers

Efficient spiking convolutional neural networks accelerator with multi-structure compatibility

1 College of Electronic Science and Technology, National University of Defense Technology, Changsha, China 2 College of Electronics and Internet of Things, Chongqing Polytechnic University of ...
C&EN

A Multi-Stage Graph Neural Network–Physics-Informed Neural Network (GNN–PINN) Framework for Thermodynamic Property Prediction

Department of Chemistry and Chemical Engineering, Education and Research Center for Smart Energy and Materials, Inha University, Incheon 22212, Republic of Korea ...
techxplore

Mice and AI neural networks reveal similar patterns when learning to cooperate

At a time when conflict and division dominate the headlines, a new study from UCLA finds remarkable similarities in how mice and artificial intelligence systems each develop cooperation: working ...
eLife

Neural dynamics of reversal learning in the prefrontal cortex and recurrent neural networks

The findings of this study are valuable, offering insights into the neural representation of reversal probability in decision-making tasks, with potential implications for understanding flexible ...