Mon May 1st through Fri the 5th
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 sponsors.
Sponsor information will appear here in the future.
|Conference Sessions and Workshops||Mon May 1st through Fri the 5th|
|Abstract Submission Deadline||Sep 21 '22(Anywhere on Earth)|
|Paper Submission deadline||Sep 28 '22(Anywhere on Earth)|
|Paper Reviews Released||Nov 05 '22 03:00 AM CAT *|
|Paper Decision Notification||Jan 21 '23 04:00 AM CAT *|
|All dates »||
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