APROVIS3D
APROVIS3D project targets analog computing for artificial intelligence in the form of Spiking Neural Networks (SNNs) on a mixed analog and digital architecture. The project includes including field programmable analog array (FPAA) and
ATLANTIS
ATLANTIS attempts to understand and model the very first stages in grounded language learning, as we see in children until the age of three: how pointing or other symbolic gestures emerge from the ontogenetic ritualization of instrumental
BANANA
Attention estimation and annotation are tasks aimed at revealing which parts of some content are likely to draw the users’ interest. Previous approaches have tackled these incredibly challenging tasks using a variety of behavioral signals
BIG-SMART-LOG
“Logistics 4.0” is referred to the combination of using logistics with the innovations and applications added by Cyber Physical Systems (CPS). “Smart Logistics” is a logistics system, which can enhance the flexibility, the adjustment to the
BITSCOPE
This project presents a vision for brain computer interfaces (BCI) which can enhance social relationships in the context of sharing virtual experiences. In particular we propose BITSCOPE, that is, Brain-Integrated Tagging for Socially
BURG
Grasping rigid objects has been reasonably studied under a wide variety of settings. The common measure of success is a check of the robot to hold an object for a few seconds. This is not enough. To obtain a deeper understanding of object
CAMOMILE
Human activity is constantly generating large volumes of heterogeneous data, in particular via the Web. These data can be collected and explored to gain new insights in social sciences, linguistics, economics, behavioural studies as well as
CausalXRL
Deep reinforcement learning systems are approaching or surpassing human-level performance in specific domains, from games to decision support to continuous control, albeit in non-critical environments. Most of these systems require random
CHASER
Channel charting (CC) is an emerging application of self-supervised machine learning (ML) to wireless communication which leverages the fact that wireless communications systems continuously collect data about the electromagnetic
CIMPLE
Explainability is of significant importance in the move towards trusted, responsible and ethical AI, yet remains in infancy. Most relevant efforts focus on the increased transparency of AI model design and training data, and on statistics