The Ninth International Conference on Learning Representations (Virtual Only)
Mon May 3rd through Fri the 7th

Announcements

Accepted Posters, Spotlights and Orals

ICLR 2021 Conference | Open Review

Affinity Group Activities

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

Call for Socials - Apply to Host a Social

 

BEWARE of Predatory ICLR conferences being promoted through the World Academy of Science, Engineering and Technology organization. Current and future ICLR conference information will be only be provided through this website and OpenReview.org.

Sponsors

The generous support of our sponsors allowed us to reduce our ticket price by about 50%, and support diversity at the meeting with travel awards. In addition, many accepted papers at the conference were contributed by our sponors.

View ICLR 2021 sponsors »Become a 2021 Sponsor »

General Chair

  • Shakir Mohamed, DeepMind

Senior Program Chair

  • Katja Hofmann, Microsoft

Program Chairs

  • Alice Oh, KAIST
  • Naila Murray, Facebook AI Research
  • Ivan Titov, U Edinburgh / U Amsterdam

Workshop Chairs

  • Sanmi Koyejo, U Illinois UC
  • Chelsea Finn, Stanford

Area Chairs

Ethics Review Committee

  • Thomas G. Dietterich, Oregon State University
  • Ayanna Howard, Georgia Institute of Technology
  • Chihyung Jeon, KAIST
  • Patrick Lin, California Polytechnic State University
  • Miguel Luengo-Oroz, UN Global Pulse
  • Margaret Mitchell, Google Research and Machine Intelligence

Diversity Equity & Inclusion Chairs

  • Jane Wang, Google
  • Emtiyaz Khan, RIKEN AIP

Virtual Chairs - Virtual & Volunteers 

  • Hendrik Strobelt, IBM
  • Matthias Gallé, Naver Labs Europe
  • Sileye Ba, Loreal
  • Marija Stanojevic, Temple University

Engagements Chair - Press, Socials & Sponsors

  • Viktoriia Sharmanska, Imperial
  • Luisa Zintgraf, University of Oxford

Contact

The organizers can be contacted here.

Important Dates

Conference Sessions Mon May 3rd through Fri the 7th
Registration Opens Feb 10 '21 02:00 PM UTC *
FinancialSupportApplicationOpen Feb 11 '21 02:00 AM UTC *
SocialsApplicationOpen Feb 17 '21 04:27 AM UTC *
SponsorExpoCall - Talks/PanelsOpen Feb 26 '21 02:00 PM UTC *
SponsorExpoCall - Demonstrations Open Feb 26 '21 02:00 PM UTC *
CameraReady Mar 17 '21(Anywhere on Earth)
SlidesLive Video Upload Deadline Mar 26 '21(Anywhere on Earth)
Socials Submission Deadline Mar 29 '21(Anywhere on Earth)
Financial Support Application Deadline Apr 02 '21 06:00 AM UTC *
Socials Notification Apr 09 '21(Anywhere on Earth)
SponsorExpoCall - Talks/PanelsClose Apr 13 '21 01:00 AM UTC *
SponsorExpoCall - Demonstrations Close Apr 13 '21 01:00 AM UTC *
ExpoCallsDeadline Apr 12 '21(Anywhere on Earth)
SponsorExpoDecisionNotification Apr 13 '21 07:00 PM UTC *
Registration Cancellation Refund Deadline Apr 30 '21 03:00 PM UTC *
All dates »

Timezone: »

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
  • implementation issues, parallelization, software platforms, hardware
  • applications in audio, speech, robotics, neuroscience, computational biology, or any other field
  • societal considerations of representation learning including fairness, safety, privacy