Measuring disentanglement: A review of metrics
MA Carbonneau, J Zaidi, J Boilard… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
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 …
artificial intelligence. While many advances have been made to learn these representations …
[HTML][HTML] Prognostics and health management of industrial assets: Current progress and road ahead
L Biggio, I Kastanis - Frontiers in Artificial Intelligence, 2020 - frontiersin.org
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 …
the Industry 4.0 revolution. Efficiently detecting whether an industrial component has …
Disentangled representation learning
Disentangled Representation Learning (DRL) aims to learn a model capable of identifying
and disentangling the underlying factors hidden in the observable data in representation …
and disentangling the underlying factors hidden in the observable data in representation …
No representation rules them all in category discovery
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 …
given a dataset with labelled and unlabelled images, the task is to cluster all images in the …
Weakly-supervised disentanglement without compromises
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 …
their environment. We model such observations as pairs of non-iid images sharing at least …
Learning disentangled representations for recommendation
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 …
latent factors behind the users' decision making processes. The factors are highly entangled …
Causalvae: Disentangled representation learning via neural structural causal models
Learning disentanglement aims at finding a low dimensional representation which consists
of multiple explanatory and generative factors of the observational data. The framework of …
of multiple explanatory and generative factors of the observational data. The framework of …
BoB: BERT over BERT for training persona-based dialogue models from limited personalized data
Maintaining consistent personas is essential for dialogue agents. Although tremendous
advancements have been brought, the limited-scale of annotated persona-dense data are …
advancements have been brought, the limited-scale of annotated persona-dense data are …
Additive decoders for latent variables identification and cartesian-product extrapolation
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 …
generation in representation learning. We show that both are possible for a class of …
Are disentangled representations helpful for abstract visual reasoning?
S Van Steenkiste, F Locatello… - Advances in neural …, 2019 - proceedings.neurips.cc
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 …
the data independently. Although it is often argued that this representational format is useful …