Frontiers

Artificial intelligence and deep learning for neural data analysis

Artificial intelligence and deep learning have revolutionized the field of neural data analysis in recent years. The explosion of complex, high-dimensional ...
IEEE

SDHGCN: A Heterogeneous Graph Convolutional Neural Network Combined With Shadowed Set

Abstract: Graph convolutional neural networks (GCNs) have demonstrated effectiveness in processing graph structure. Due to the diversity and complexity of real-world graph data, heterogeneous GCN have ...
GitHub

DKledx/Learning-Deep-Learning-Specialization-Coursera

This repo contains all my work for this specialization. All the code base, quiz questions, screenshot, and images, are taken from, unless specified, Deep Learning Specialization on Coursera. As a CS ...
C&EN

NN-VRCTST: Neural Network Potentials Meet Variable Reaction Coordinate Transition State Theory for the Rate Constant Determination of Barrierless Reactions

MOX, Modeling and Scientific Computing, Dipartimento di Matematica, Politecnico di Milano, 20133 Milano, Italy ...
IEEE

Adaptive Hierarchical Multi-Headed Convolutional Neural Network With Modified Convolutional Block Attention for Aerial Forest Fire Detection

Abstract: Effective detection and classification of forest fire imagery are critical for timely and efficient wildfire management. Convolutional Neural Networks (CNNs) have demonstrated potential in ...
C&EN

Grand-Canonical Equivariant Neural Potentials for Electrochemical Interfaces

State Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China Department of Chemical Physics, School of Chemistry and Materials ...