ICLR @ New Orleans ·The Seventh International Conference on Learning Representations
Mon May 6th through Thu the 9th Tourism in New Orleans »

Videos

We have posted videos for many of the talks that were lived streamed. These videos are linked into the schedule (see the Schedule section above). They are also collected here

Affinity Group Activities

Please visit the Diversity & Inclusion link for scheduled program and activities.

Registration 

Pricing » Register 01/21 » 

Child Care » Hotels » Visa Information »

Registration Cancellation Policy »

Sponsors

The ICLR 2020 Sponsor Portal will open in the last quarter of 2019.  New sponsors to ICLR, please email us at 2020 ICLR Sponsor Prospectus Request, provide your contact information and you will receive the ICLR 2020 Sponsor Prospectus when it becomes available.  If you were a sponsor at ICLR 2019 then you will receive the ICLR 2020 Sponsor Prospectus via email when it becomes available as well. 
 
 

Important Dates

Conference Sessions Mon May 6th through Thu the 9th
Early pricing before this date. March 19, 2019, 4:59 p.m. *
Last chance for a refund on registration fees April 18, 2019, 4:59 p.m. *
All dates » * Dates above are in pacific time

About Us

The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning.

ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics.

Participants at ICLR span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

A non-exhaustive list of relevant topics explored at the conference include:

  • unsupervised, semi-supervised, and supervised representation learning
  • representation learning for planning and reinforcement learning
  • metric learning and kernel learning
  • sparse coding and dimensionality expansion
  • hierarchical models
  • optimization for representation learning
  • learning representations of outputs or states
  • implementation issues, parallelization, software platforms, hardware
  • applications in vision, audio, speech, natural language processing, robotics, neuroscience, or any other field