ICLR 2025
The Thirteenth International Conference on Learning Representations
Singapore EXPO
Thu Apr 24 – Mon Apr 28th, 2025
Announcements:
Important Dates
Abstract Submission Deadline | Sep 27 '24 (Anywhere on Earth) |
Full Paper Submission Deadline | Oct 01 '24 (Anywhere on Earth) |
- BEWARE of Predatory ICLR conferences being promoted through the World Academy of Science, Engineering and Technology organization.
Sponsors
Sponsor application details will be posted soon.
Latest ICLR Blog Entries [ All Entries ]
Aug 22, 2024 | Extended partnership pilot with TMLR for ICLR 2025 |
May 07, 2024 | ICLR 2024 Test of Time Award |
May 07, 2024 | ICLR 2024 Outstanding Paper Awards |
May 06, 2024 | Code of Ethics Cases at ICLR 2024 |
May 01, 2024 | ICLR 2024 Mentoring Chats |
Apr 22, 2024 | Hugging Face Demo Site |
Apr 15, 2024 | Announcing ICLR 2024 Invited Speakers |
Apr 02, 2024 | Blogposts Track ICLR 2024 : Announcing Accepted Blogposts |
Jan 08, 2024 | Announcing the Accepted Workshops at ICLR 2024 |
Nov 02, 2023 | Tiny Papers Strike Back: 2023 Reflections and 2024 Announcement |
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