Workshop on Self‐Supervised Learning for Signal Decoding 2022

Description

To date, most successful applications of deep learning in signals and decoding are based on supervised learning. However, supervised learning is contingent on the availability of labeled data. The need for labeled data is a serious limitation to applications at scale and complicates the maintenance of real‐life supervised learning systems. The typical situation is that unlabeled data is abundant, and this has given rise to paradigms such as self‐supervised learning (SSL). SSL is based on combining large amounts of unlabeled data with limited labeled data and relies on supervised learning with a supervisory signal derived from the data itself. While we have seen many successful applications of SSL, theoretical understanding of the mechanisms that make SSL work is only starting to emerge.      In this workshop, we will discuss both the emerging theoretical understanding and practical applications of SSL in decoding of complex signals.       The technical program features invited talks, posters, in addition to social activities.

 

Organizers

Zheng‐Hua Tan and Lars Kai Hansen, Signals and Decoding Co‐laboratory, Pioneer Centre for AI, Denmark

 

Registration

Registration is done by filling in the attached Excel file and send to Susanne Nørrevang sn@es.aau.dk.

 

Deadline: October 1, 2022.

Registration is free with lunches, dinner and coffee breaks included. There is a no‐show fee of DKK 2000. Cancellations are accepted no later than October 6, 2022.