This project implements a drug-disease association prediction model using Graph Convolutional Networks (GCN) with advanced data augmentation techniques. The model predicts novel drug-disease ...
A new technical paper “AutoGNN: End-to-End Hardware-Driven Graph Preprocessing for Enhanced GNN Performance” was published by researchers at KAIST, Panmnesia, Peking University, Hanyang University, ...
UiPath is the world's largest robotic process automation (RPA) company. Its AI-powered software robots can be plugged into an organization's existing software to automate repetitive tasks such as data ...
Think about someone you’d call a friend. What’s it like when you’re with them? Do you feel connected? Like the two of you are in sync? In today’s story, we’ll meet two friends who have always been in ...
Abstract: Graph embeddings map graph-structured data into vector spaces for machine learning tasks. In Graph Neural Networks (GNNs), these embeddings are computed through message passing and support ...
Abstract: Gas distribution mapping (GDM) is essential for industrial safety and environmental monitoring, as it enables real-time hazard detection and air quality assessment. Traditional GDM methods, ...
Adapting to the stream: An instance-attention GNN method for irregular multivariate time series data
Framework of DynIMTS. The model is a recurrent structure based on a spatial-temporal encoder and consists of three main components: embedding learning, spatial-temporal learning, and graph learning.
Alongside text-based large language models (LLMs), including ChatGPT in industrial fields, GNN (Graph Neural Network)-based graph AI models that analyze unstructured data such as financial ...
A new technical paper titled “TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs” was published by researchers at University of Connecticut and University of Minnesota. “hip ...
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