Learning representations of multimodal data that are both informative and robust to missing
modalities at test time remains a challenging problem due to the inherent heterogeneity of …

Most multimodal learning methods assume that all modalities are always available in data.
However, in real-world applications, the assumption is often violated due to privacy …

Multimodal VAEs have recently gained attention as efficient models for weakly-supervised
generative learning with multiple modalities. However, all existing variants of multimodal …

When applying multimodal machine learning in downstream inference, both joint and
coordinated multimodal representations rely on the complete presence of modalities as in …

Multimodal datasets are ubiquitous in modern applications, and multimodal Variational
Autoencoders are a popular family of models that aim to learn a joint representation of …

Research on general multimodal systems has gained significant attention due to the
proliferation of multimodal data in the real world. Despite the remarkable performance …

Neural Networks special issue on Artificial Intelligence and Brain Science | Neural Networks
skip to main content ACM Digital Library home ACM home Google, Inc. (search) Advanced …

Devising deep latent variable models for multi-modal data has been a long-standing theme
in machine learning research. Multi-modal Variational Autoencoders (VAEs) have been a …

In recent years, there has been a major boom in the development of multimodal machine
learning models. Among open topics, representation (fusion) and generation of multimodal …

In machine learning, a sub-field of computer science, a two-tower architecture model is a
specialised type of neural network model that encodes paired data from different modalities …