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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 »(not currently taking applications)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 - Socials & Sponsors
- Viktoriia Sharmanska, Imperial
- Luisa Zintgraf, University of Oxford
Contact
The organizers can be contacted here.
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