Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …

Communication is an effective mechanism for coordinating the behavior of multiple agents.
In the field of multi-agent reinforcement learning, agents can improve the overall learning …

This paper proposes a novel contrastive learning framework, called FOCAL, for extracting
comprehensive features from multimodal time-series sensing signals through self …

Multimodal magnetic resonance imaging (MRI) provides complementary information for sub-
region analysis of brain tumors. Plenty of methods have been proposed for automatic brain …

Recently, numerous studies have demonstrated the effectiveness of contrastive learning
(CL), which learns feature representations by pulling in positive samples while pushing …

Recently, multimodal fusion efforts have achieved remarkable success in Multimodal
Sentiment Analysis (MSA). However, most of the existing methods are based on model-level …

Contrastive learning is a cornerstone underlying recent progress in multi-view and
multimodal learning, eg, in representation learning with image/caption pairs. While its …

Multi-view clustering methods group data into different clusters by discovering the
consensus in heterogeneous sources, which however becomes difficult when partial views …

Abstract Electronic Health Record (EHR) provides abundant information through various
modalities. However, learning multi-modal EHR is currently facing two major challenges …

This paper explores the tasks of leveraging auxiliary modalities which are only available at
training to enhance multimodal representation learning through cross-modal Knowledge …