Skip to yearly menu bar Skip to main content

ICLR 2019 · The Seventh International Conference on Learning Representations


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.


Pricing » Register 11/18  

Child Care » Hotels » Visa Information »

Registration Cancellation Policy »


The ICLR 2020 Sponsor Portal will open on December 23, 2020.  The Sponsor Portal can be accessed through the ICLR Sponsor Information page found here: ICLR Sponsor Information

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.

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
  • representation learning for computer vision and natural language processing
  • metric learning and kernel learning
  • sparse coding and dimensionality expansion
  • hierarchical models
  • optimization for representation learning
  • learning representations of outputs or states
  • optimal transport
  • theoretical issues in deep learning
  • societal considerations of representation learning including fairness, safety, privacy, and interpretability, and explainability
  • visualization or interpretation of learned representations
  • implementation issues, parallelization, software platforms, hardware
  • climate, sustainability
  • applications in audio, speech, robotics, neuroscience,  biology, or any other field