Call for Abstracts 

Important dates

Call for Abstracts

Please join for the 8th Northern Lights Deep Learning Conference (NLDL) in Tromsø, Norway, organized by Visual Intelligence and the UiT Machine Learning Group. 

We look forward to gathering the deep learning community again in the cool arctic air for a physical conference. 

We invite submissions presenting new and original research on all aspects of Deep Learning. The topics include but are not limited to the following: 

As always, we are happy to have top international speakers. This year, for instance 

A tentative program will be available soon. We hope to see many participants for a nice scientific gathering on the “north pole”, including social events, and hopefully some northern lights  

How to submit a contribution

We invite participants to submit in PDF format a 2-page extended abstract (non-archival) in the area of Deep Learning. References can go beyond the page limit.

All submissions will undergo double-blind peer review. It will be up to the authors to ensure the proper anonymization of their paper. Do not include any names or affiliations. Refer to your own past work in the third-person, or if needed insert a blank reference. Accepted contributions will be accepted as contributing talks or poster presentations.

You can download the latest packaged template here or follow the instructions in the repository to clone the template.  Detailed style guidelines can be found within the main.tex or in the compiled versions here.

Reference instructions

Additional information

Follow the following link to submit your extended abstracts in Open Review:


The extended abstracts will not be archived.  Consequently, they won't appear on the proceedings.

Questions & Problems

For questions about the submission process, check the Author FAQ.  For problems with the submission side, please contact the program chairs:

Contacting PCs: the PCs will be fully dedicated to ensuring the proper allocation of papers to suitable ACs and reviewers and to overseeing the review process. PCs will strictly adhere to the following policies: