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Found 8 papers, 1 papers with code
A Comparative Study on Enhancing Prediction in Social Network Advertisement through Data Augmentation
In the ever-evolving landscape of social network advertising, the volume and accuracy of data play a critical role in the performance of predictive models.
Exploring Diverse Methods in Visual Question Answering
This study explores innovative methods for improving Visual Question Answering (VQA) using Generative Adversarial Networks (GANs), autoencoders, and attention mechanisms.
Self-explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction
Objective: Our goal is to utilize Graph Neural Networks (GNNs) with claims data for ADRD risk prediction.
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
Graph representation learning (GRL) has emerged as a pivotal field that has contributed significantly to breakthroughs in various fields, including biomedicine.
Globally Interpretable Graph Learning via Distribution Matching
Graph neural networks (GNNs) have emerged as a powerful model to capture critical graph patterns.
Mining On Alzheimer's Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing
The 1, 672, 110 filtered triples were used to train with knowledge graph completion algorithms (i. e., TransE, DistMult, and ComplEx) to predict candidates that might be helpful for AD treatment or prevention.
Knowledge Graph-based Neurodegenerative Diseases and Diet Relationship Discovery
To date, there are no effective treatments for most neurodegenerative diseases.
AutoML using Metadata Language Embeddings
We use these embeddings in a neural architecture to learn the distance between best-performing pipelines.
