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
General Chair
- Tara Sainath, Google
Senior Program Chair
- Alexander Rush, Harvard University
Program Chairs
- Sergey Levine, UC Berkeley
- Karen Livescu, TTI-Chicago
- Shakir Mohamed, Google DeepMind
Workshop Chairs
- Been Kim, Google Brain
- Graham Taylor, University of Guelph / Vector Institute
Diversity+Inclusion Chairs
- Alice Oh, KAIST
- Richard Zemel, University of Toronto / Vector Institute
Contact
The organizers can be contacted here.
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
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