Important dates

(All deadlines are 23:59 Oslo Time) 

• Submission: Mid-September 2026 (exact date to be announced)

Deadlines for reviews, emergency reviews, rebuttals etc. will be announced

Call for Abstracts

The 2027 edition of the Northern Lights Deep Learning Conference (NLDL) will take place on 12-14 January 2027 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.

The conference includes scientific topics, an industry event, diversity in AI event, and transferable skills. The NLDL winter school, which is a part of the NORA research school http://nora.ai, starts on Jan 11, ends on Jan 15, and incorporates events during the main conference days. The winter school tutorials will be hosted by internationally leading experts. More information about Winter School program and speakers coming soon at Winter School.

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

●        General Deep Learning (architectures, generative models, deep reinforcement learning, etc.)

●        Machine Learning related to deep learning (active learning, clustering, online learning, ranking, reinforcement learning, supervised, semi- and self-supervised learning, time series analysis, etc.)

●        Optimization (convex and non-convex optimization, matrix/tensor methods, stochastic, online, non-smooth, composite, etc.)

●        Methods (Bayesian methods, variational inference, graphical models, explainability methods, etc.)

●        Applications to vision, language, signals, speech and audio, etc.

●        Deep Learning for Sciences (biology and medicine, environment and ecology, physics, etc.)

As always, we are happy to have top international speakers as keynote speakers. More information about speakers at Keynotes.

Deadline for extended abstracts: Mid-September 2026 (exact date to be announced)

How to submit an abstract 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 single-blind peer review. Accepted contributions will be accepted as non-archival contributing talks or poster presentations.

Submissions must be PDF files that follow the template we provide. The template and guidelines will be released soon.   

Reference instructions

• References need to include a DOI (if it exists)

• When citing arXiv papers, the authors need to check if the paper has been published. If there is a published version of the paper, it should be cited instead of the arXiv version.

Additional information

• All authors must have an active account in OpenReview (no exceptions).

• Authors cannot be added to the papers once the submission is made (no exceptions).

• We allow authors to upload a preprint of the submitted papers to arXiv

• There will be no publication for the abstracts.

• At least one author must register for the conference and present their work in person.

• Authors must read the guidelines here.

Proceedings

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

Copyright for accepted papers

Authors of accepted extended abstracts (non-archival submissions) retain full copyright of their work, and acceptance of such a submission does not preclude publication of the same material in another journal or conference.

Questions & Problems

For questions about the submission process, check the Author FAQ.  For problems with the submission side, please contact the program chairs: nldl.conf@gmail.com. 

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:

• • Late submissions will not be accepted under any circumstances.

  • Submissions of papers, or updates to papers, via email will not be accepted.

  • Requests to modify lists of authors will not be considered, regardless of the justification provided.

  • Detailed elucidation of the policies will not be provided.

  • Questions already answered in the FAQ will not be answered by contacting the PCs.