Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Deep neural networks excel in supervised learning tasks but are constrained by the need for
extensive labeled data. Self-supervised learning emerges as a promising alternative …

In this article, we propose a unified framework to solve the following two challenging
problems in incomplete multi-view representation learning: i) how to learn a consistent …

In this paper, we study two challenging problems in incomplete multi-view clustering
analysis, namely, i) how to learn an informative and consistent representation among …

We introduce a conceptually simple yet effective model for self-supervised representation
learning with graph data. It follows the previous methods that generate two views of an input …

We propose an approach to self-supervised representation learning based on maximizing
mutual information between features extracted from multiple views of a shared context. For …

In a wide range of multimodal tasks, contrastive learning has become a particularly
appealing approach since it can successfully learn representations from abundant …

Multi-view learning is an emerging direction in machine learning which considers learning
with multiple views to improve the generalization performance. Multi-view learning is also …

We hypothesize that due to the greedy nature of learning in multi-modal deep neural
networks, these models tend to rely on just one modality while under-fitting the other …

The last years have witnessed the emergence of a promising self-supervised learning
strategy, referred to as masked autoencoding. However, there is a lack of theoretical …