Apr 3, 2023 · We introduce a blueprint for multimodal graph learning (MGL). The MGL blueprint provides a framework that can express existing algorithms and help develop new methods for …

Feb 7, 2024 · Machine learning on multimodal graphs, referred to as multimodal graph learning (MGL), is essential for successful artificial intelligence (AI) applications. The burgeoning …

We introduce the multimodal graph learning (MGL) blueprint that serves as a unifying framework for multimodal graph neural architectures realized through learning systems in computer …

Here, we survey 145 studies in graph AI and realize that diverse datasets are increasingly combined using graphs and fed into sophisticated multimodal methods, specified as image …

Jan 11, 2022 · In the process, we’ll learn how to use graph ML techniques to perform multimodal learning — learning joint representations of multiple data modalities — and implement them …

To address these challenges, multimodal graph AI methods combine different modalities while leveraging cross-modal dependencies using graphs. Diverse datasets are combined using …

When applying machine learning to tackle realworld problems, it is common to see that objects come with multiple labels rather than a single label. In addition,

Nov 6, 2005 · In this paper, it is studied from a graph point of view: each kind of feature from one modality is represented as one independent graph; and the learning task is formulated as …

Jul 1, 2021 · In this paper we argue for using Graph Neural Networks as a method-of-choice, enabling information fusion for multi-modal causability (causability – not to confuse with …

Sep 7, 2022 · Learning on multimodal datasets presents fundamental challenges because the inductive biases can vary by data modality and graphs might not be explicitly given in the …