An old problem in multivariate statistics is that linear Gaussian models are often
unidentifiable. In factor analysis, an orthogonal rotation of the factors is unidentifiable, while …

This review article explores how emerging generative artificial intelligence (GenAI) models,
such as large language models (LLMs), can enhance solution methodologies within process …

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 …

Independent component analysis provides a principled framework for unsupervised
representation learning, with solid theory on the identifiability of the latent code that …

Unsupervised learning of latent variable models (LVMs) is widely used to represent data in
machine learning. When such model reflects the ground truth factors and the mechanisms …

In disentangled representation learning, a model is asked to tease apart a dataset's
underlying sources of variation and represent them independently of one another. Since the …

We develop the sparse VAE for unsupervised representation learning on high-dimensional
data. The sparse VAE learns a set of latent factors (representations) which summarize the …

Variational autoencoders (VAEs) are a popular framework for modeling complex data
distributions; they can be efficiently trained via variational inference by maximizing the …

Neurons in the brain are often finely tuned for specific task variables. Moreover, such
disentangled representations are highly sought after in machine learning. Here we …

Learning representations that generalize to novel compositions of known concepts is crucial
for bridging the gap between human and machine perception. One prominent effort is …