Abstract: Graphs are ubiquitous for modeling complex systems involving structured data and relationships. Consequently, graph representation learning, which aims to automatically learn low-dimensional ...

Many successful machine learning models for molecular property prediction rely on Lewis structure representations, commonly encoded as SMILES strings. However, a key limitation arises with molecules ...

Fuzzy set theory, an extension of classical set theory, provides a mathematical framework for handling uncertainty and imprecision. This paper provides some key properties of fuzzy sets, emphasizing ...

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...

Abstract: Marine transportation constitutes a vital segment of international trade logistics. Recognizing marine carrier ship types is essential for the governance and efficiency of the marine ...

In today’s Data Storytelling Visualization journey, we learn to avoid making the same mistakes as the past; not every graph/chart needs to highlight groundbreaking insights, and we must deal with the ...

Advances in Chemical Representations and AI in Drug Discovery: The past century’s technological advancements, especially the computer revolution and high-throughput screening in drug discovery, have ...