Complex and Simple Models of Multidimensional Data : from graphs to neural networks

Real-life datasets are characterized by a variety of geometries, topologies, ambient and intrinsic dimensionalities. In order to deal with this variety and complexity, we need to develop appropriate theoretical models of the data, able to capture their properties. Graph-based (such as principal graphs based on application of topological grammars) and neural network-based (such as non-linear autoencoders) methods have become popular recently in machine learning field and in practical applications such as the analysis of single cell molecular data. Simpler data models can be easier to manage and interpret but can miss important aspects of geometrical multidimensional data organization, while more complex models might be difficult to train and avoid overfitting. Any practical data analysis should determine a match between the data and the data model complexities. The purpose of this workshop is to collect early career and experienced researchers interested in the questions related to dealing with multidimensional data and data models.