The LDS model is built on the state-space model and assumes latent factors evolvewith linear dynamics. Ontheother hand, GPFAmodels thelatent vectors by non-parametric Gaussian processes.

The application of reinforcement learning (RL) involving interactions with individuals has grown significantly in recent years.

The existing graph neural architecture search (GNAS) methods heavily rely on supervised labels during the search process, failing to handle ubiquitous scenarios where supervisions are not available. In this paper, we study the problem of unsupervised graph neural architecture search, which remains unexplored inthe literature. The key problem is to discover the latent graph factors that drive the formation of graph data as well as the underlying relations between the factors andtheoptimal neural architectures.

Contrastive learning has recently emerged as a promising approach for learning data representations that discover and disentangle the explanatory factors of the data.

Data coming from different sensors often capture information related to common latent factors.

Neural Information Processing Systems http://nips.cc/

Tensor decompositions are fundamental tools for multiway data analysis.

InMTL, there areDdifferent labeled domains where data are related and the goal is to improve the predictive power of all domains altogether.