The Tenth International Conference on Learning Representations (Virtual) Mon Apr 25th through Fri the 29th


ICLR 2022 Important Dates

• Abstract submission: 29 September 2021, 12:00AM UTC
• Submission date: 6 October 2021, 12:00AM UTC
• Reviews released: 8 November 2021
• Author discussion period ends: 22 November 2021
• Final decisions: 24 January 2022

BEWARE of Predatory ICLR conferences being promoted through the World Academy of Science, Engineering and Technology organization. Current and future ICLR registration and 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.

2022 ICLR Organizing Committee
General Chair

Katja Hofmann, Microsoft
Senior Program Chair

Yan Liu, University of Southern California
Program Chairs

Chelsea Finn, Stanford University
Yejin Choi, University of Washington / AI2
Marc Deisenroth, University College London
Workshop Chairs

Feryal Behbahani, DeepMind
Vukosi Marivate, University of Pretoria
Area Chairs

Area Chairs »
Ethics Review Committee

Coming soon

Diversity Equity & Inclusion Chairs

Krystal Maughan, University of Vermont
Rosanne Liu, Google & ML Collective
Virtual Chairs - Virtual & Volunteers

Coming soon
Engagements Chair - Socials & Sponsors

Ehi Nosakhare, Microsoft
William Agnew, University of Washington
Contact

The organizers can be contacted here.
https://iclr.cc/Help/Contact?select=OpenReview

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