ICLR 2027
The Fifteenth International Conference on Learning Representations
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Latest Announcements
- July 28: Call for papers is posted with associated author, reviewer, AC, and SAC guides under the Guides menu above.
Venue
Venue information will be posted here
The conference is a five-day multi-track event with a mix of invited talks, oral paper presentations, and poster sessions. The fourth and fifth day are workshops.
Exhibitors
We thank our exhibitors for all their support!
Information on becoming an exhibitor will be posted here. See 2026 Exhibitors.
Important Dates
About the Conference
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.
ICLR is one of the fastest growing artificial intelligence conferences in the world. Participants at ICLR span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
The rapidly developing field of deep learning is concerned with questions surrounding how we can best learn meaningful and useful representations of data. ICLR takes a broad view of the field and includes topics such as feature learning, metric learning, compositional modeling, structured prediction, reinforcement learning, and issues regarding large-scale learning and non-convex optimization.
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
- 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 Vision, Audio, Speech, Natural Language Processing, Robotics, Neuroscience, or Any Other Field