Time-Series Mining (TSM) is an important research area since it shows great potential in
practical applications. Deep learning models that rely on massive labeled data have been …

A central problem in unsupervised deep learning is how to find useful representations of
high-dimensional data, sometimes called" disentanglement." Most approaches are heuristic …

The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …

Self-supervised representation learning has shown remarkable success in a number of
domains. A common practice is to perform data augmentation via hand-crafted …

Causal representation learning seeks to extract high-level latent factors from low-level
sensory data. Most existing methods rely on observational data and structural assumptions …

We study causal representation learning, the task of inferring latent causal variables and
their causal relations from high-dimensional functions (“mixtures”) of the variables. Prior …

In this work, we investigate an intriguing and prevalent phenomenon of diffusion models
which we term as" consistent model reproducibility'': given the same starting noise input and …

The framework of variational autoencoders allows us to efficiently learn deep latent-variable
models, such that the model's marginal distribution over observed variables fits the data …

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 …

Vital signs are crucial indicators for human health, and researchers are studying contact-free
alternatives to existing wearable vital signs sensors. Unfortunately, most of these designs …