This paper explores the challenge of learning from heterogeneous data, particularly trajectory and tabular data, and proposes approaches for representation ...

This paper explores the challenge of learning from heterogeneous data, particularly trajectory and tabular data, and proposes approaches for representation ...

This paper delves into the intricacies of representing spatio- temporal tabular data within a single model and offers inno- vative approaches for merging ...

Summary A Spatio-temporal data model for biodiversity is growing importance to the biodiversity data management, forest and environment control.

During learning, spatio-temporal decision trees are generated which satisfy relational constraints of the training data. The resulting rules are used to ...

Abstract. Deep learning applies hierarchical layers of hidden variables to construct nonlinear high dimensional predictors. Our goal is to develop and train ...

W e introduce a rule-based approach for learning and recognition of complex actions in terms of spatio-temporal attributes of primitive event sequences.

We introduce a rule-based approach for learning and recognition of complex actions in terms of spatio-temporal attributes of primitive event sequences.

Mar 19, 2023 · Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from ...

Dec 17, 2020 · We introduce here a framework for spatio-temporal prediction of climate and environmental data using deep learning.