Real-world applications of machine learning often face challenges due to two main issues which recur in many application scenarios: the cost of acquiring reliable, large, labeled training datasets; and the difficulty in generalizing trained models to the deployment domain. The tutorial will cover a set of state-of-the-art techniques to overcome these issues.
First, we discuss several successful examples of self-supervised learning, a classic approach in robotics which consists in the automated acquisition of ground truth labels by exploiting multiple sensors during the robot’s operation; more recently, a related but broader line of research has grown in the field of deep learning, which aims to use the data itself as a supervisory signal, based on simple, intuitive ideas with compelling results.
Then, we delve into domain adaptation techniques, which tackle the issue of handling differences between the training and the deployment domains; this is a key challenge in many practical applications, where large datasets are available (or cheap to acquire) in some domain (e.g. in simulations), but models must be deployed in a different domain (e.g. the real world) where labeled training data is expensive. This section of the tutorial will feature hands-on experiments by implementing state-of-the-art techniques.

This event will be online and organized via Microsoft Teams.

In order to attend the seminar, please, go to the following link: Join Microsoft Teams Meeting.