Graph Neural Network contextual embedding for Deep Learning on Tabular Data

All industries are trying to leverage Artificial Intelligence (AI) based on their existing big data which is available in so called tabular form, where each record is composed of a number of heterogeneous continuous and categorical columns also known as features. Deep Learning (DL) has consituted a major breathrough for AI in fields related to human skills like natural language processing, but its applicability to tabular data has been more challenging. More classical Machine Learning (ML) models like tree-based ensemble ones usually perform better. In this manuscript a novel DL model that uses Graph Neural Network (GNN), more specifically Interaction Network (IN), for contextual embedding is introduced. Its results outperform those of the recently published survey with DL benchmark based on five public datasets, achieving also competitive results when compared to boosted-tree solutions.

Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space

In this paper, we study the problem of locating a predefined sequence of patterns in a time series. In particular, the studied scenario assumes a theoretical model is available that contains the expected locations of the patterns. This problem is found in several contexts, and it is commonly solved by first synthesizing a time series from the model, and then aligning it to the true time series through dynamic time warping. We propose a technique that increases the similarity of both time series before aligning them, by mapping them into a latent correlation space. The mapping is learned from the data through a machine-learning setup. Experiments on data from non-destructive testing demonstrate that the proposed approach shows significant improvements over the state of the art.