This survey systematically reports the contributions of Visual Analytics for eXplainable Deep Learning, spots gaps, serves as an anthology of visual analytical solutions ready to be exploited by the Deep Learning community, proves the degree of maturity, ease of integration, results for specific domains and finally identifies future research challenges.

Modelling relationship between entities in real-world systems with a simple graph is a standard approach. However, reality is better embraced as several interdependent subsystems (or layers). Recently, the concept of a multilayer network model has emerged from the field of complex systems. This model can be applied to a wide range of real-world data sets. Examples of multilayer networks can be found in the domains of life sciences, sociology, digital humanities and more. Within the domain of graph visualization, there are many systems which visualize data sets having many characteristics of multilayer graphs. This report provides a state of the art and a structured analysis of contemporary multilayer network visualization, not only for researchers in visualization, but also for those who aim to visualize multilayer networks in the domain of complex systems, as well as those developing systems across application domains.

We present ZeroEGGS, a neural network framework for speech-driven gesture generation with zero-shot style control by example. We release our code and our high-quality dataset of full-body gesture motion including fingers, with speech, spanning across 19 different styles.