ICLR 2026
Hosted by Vivian Chen, Ayoub Tabout, Ruta Desai
Every breakthrough in AI started with a question. ICLR 2026 is where the world’s sharpest minds gather to ask the next ones.
The International Conference on Learning Representations (ICLR) is the world’s leading gathering of professionals pushing the boundaries of representation learning, the field of artificial intelligence most people know as deep learning. This is where the research that shapes the future of AI gets presented, debated, and published first.
Researchers worldwide turn to ICLR to present and publish the discoveries pushing deep learning forward across artificial intelligence, statistics, and data science, and into real-world fields like computer vision, computational biology, speech recognition, natural language understanding, gaming, and robotics.
And it’s growing fast! ICLR is one of the fastest-growing AI conferences in the world, drawing participants from every corner of the field: academic and industrial researchers, entrepreneurs and engineers, graduate students and postdocs.
The field of deep learning moves quickly because the questions it asks are fundamental:
- how do machines best learn meaningful, useful representations of data?
ICLR explores every angle from feature learning and metric learning to compositional modeling, structured prediction, reinforcement learning, and the hard mathematical problems of large-scale learning and non-convex optimization.
Topics explored at ICLR 2026 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 States1
- Implementation Issues, Parallelization, Software Platforms, Hardware
- Applications in Vision, Audio, Speech, Natural Language Processing, Robotics, Neuroscience, or Any Other Field
If you are serious about deep learning, this is the room you need to be in!
Booth
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