IEEE

Parametric Activation Functions for Neural Networks: A Tutorial Survey

Abstract: Activation functions are pivotal in neural networks, determining the output of each neuron. Traditionally, functions like sigmoid and ReLU have been static and deterministic. However, the ...
C&EN

Development of Coarse-Grained Lipid Force Fields Based on a Graph Neural Network

Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong China Department of Physics, City University of Hong Kong, Kowloon 999077, Hong Kong China ...
IEEE

Heterogeneous Graph Convolutional Neural Networks for EEG-fNIRS Bimodal Emotion Recognition

Abstract: Leveraging multimodal brain signals, such as electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS), for the objective detection of brain activity is regarded as a ...
GitHub

Subgraph-conditioned Graph Information Bottleneck

We aim to build a pre-trained Graph Neural Network (GNN) model on molecules without human annotations or prior knowledge. Although various attempts have been proposed to overcome limitations in ...
GitHub

Graph Neural Prompting with Large Language Models

Large language models (LLMs) have shown remarkable generalization capability with exceptional performance in various language modeling tasks. However, they still exhibit inherent limitations in ...