ICLR @ Addis Ababa ·The Eighth International Conference on Learning Representations
Sun Apr 26th through Thu the 30th

Affinity Group Activities

Please visit the Diversity & Inclusion page for scheduled program and activities.


Register 01/21 » 

Registration Cancellation Policy »



Important Dates

Sponsor Portal Open Sept. 17, 2019, 8 a.m. *
Paper Submission deadline Sept. 25, 2019, 8 a.m. *
Paper Rebuttal/discussion ends Nov. 15, 2019, 2 p.m. *
Paper Decision Notification Dec. 19, 2019, 2 p.m. *
Registration Opens Jan. 21, 2020, 8 a.m. *
All dates » * Dates above are in pacific time
Workshops Sun Apr 26th
Conference Sessions Sun Apr 26th through Thu the 30th

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
  • 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