Learning to disentangle and represent factors of variation in data is an important problem in
artificial intelligence. While many advances have been made to learn these representations …

Prognostic and Health Management (PHM) systems are some of the main protagonists of
the Industry 4.0 revolution. Efficiently detecting whether an industrial component has …

Disentangled Representation Learning (DRL) aims to learn a model capable of identifying
and disentangling the underlying factors hidden in the observable data in representation …

In this paper we tackle the problem of Generalized Category Discovery (GCD). Specifically,
given a dataset with labelled and unlabelled images, the task is to cluster all images in the …

Intelligent agents should be able to learn useful representations by observing changes in
their environment. We model such observations as pairs of non-iid images sharing at least …

User behavior data in recommender systems are driven by the complex interactions of many
latent factors behind the users' decision making processes. The factors are highly entangled …

Learning disentanglement aims at finding a low dimensional representation which consists
of multiple explanatory and generative factors of the observational data. The framework of …

We tackle the problems of latent variables identification and" out-of-support''image
generation in representation learning. We show that both are possible for a class of …

Maintaining consistent personas is essential for dialogue agents. Although tremendous
advancements have been brought, the limited-scale of annotated persona-dense data are …

A disentangled representation encodes information about the salient factors of variation in
the data independently. Although it is often argued that this representational format is useful …