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Found 4 papers, 3 papers with code
$\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning
In this work, we propose $\texttt{MiniMol}$, a foundational model for molecular learning with 10 million parameters.
Towards Principled Graph Transformers
Graph learning architectures based on the k-dimensional Weisfeiler-Leman (k-WL) hierarchy offer a theoretically well-understood expressive power.
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Recently, pre-trained foundation models have enabled significant advancements in multiple fields.
Attending to Graph Transformers
Recently, transformer architectures for graphs emerged as an alternative to established techniques for machine learning with graphs, such as (message-passing) graph neural networks.
