The Eleventh International Conference on Learning Representations
Mon May 1 — Fri May 5
- Authors of eligible TMLR publications can submit a request to present in ICLR conference! Find more here.
- Self-nomination form for ICLR 2024 Reviewing. Interested in being a reviewer? Fill out the form!
- BEWARE of Predatory ICLR conferences being promoted through the World Academy of Science, Engineering and Technology organization.
We are very excited to be holding the ICLR 2024 annual conference in Kigali, Rwanda this year from May 1-5, 2023. The conference will be located at the beautiful Kigali Convention Centre / Radisson Blu Hotel location which was recently built and opened for events and visitors in 2016. The Kigali Convention Centre is located 5 kilometers from the Kigali International Airport.
The in-person conference will also provide viewing and virtual participation for those attendees who are unable to come to Kigali, including a static virtual exhibitor booth for most sponsors.
We look forward to answering any questions you may have, and hopefully seeing you in Kigali.
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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