DeepLearn 2022 Winter will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of deep learning.
Graduate students, postgraduate students and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees, so people less or more advanced in their career will be welcome as well.
- Participants will be able to freely choose the courses they wish to attend as well as to move from one to another.
Full in vivo online participation will be possible. However, the organizers want to emphasize the importance of face to face interaction and networking in this kind of research training event.
An open session will collect 5-minute voluntary presentations of work in progress by participants.
They should submit a half-page abstract containing the title, authors, and summary of the research to firstname.lastname@example.org by January 9, 2022.
A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry. Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed.
People in charge of the demonstration must register for the event. Expressions of interest have to be submitted to email@example.com by January 9, 2022.
Firms searching for personnel well skilled in deep learning will have a space reserved for one-to-one contacts.
It is recommended to produce a 1-page .pdf leaflet with a brief description of the company and the profiles looked for to be circulated among the participants prior to the event.
People in charge of the search must register for the event. Expressions of interest have to be submitted to firstname.lastname@example.org by January 9, 2022.
- David Silva (London, co-chair)
QUESTIONS AND FURTHER INFORMATION
- A certificate of successful participation in the event will be delivered indicating the number of hours of lectures.