ICLR @ ·The Sixth International Conference on Learning Representations
Monday April 30 -- Thursday May 03, 2018      


The performance of machine learning methods is heavily dependent on the choice of data representation (or features) on which they are applied. The rapidly developing field of deep learning is concerned with questions surrounding how we can best learn meaningful and useful representations of data. We take a broad view of the field and include topics such as feature learning, metric learning, compositional modeling, structured prediction, reinforcement learning, and issues regarding large-scale learning and non-convex optimization. The range of domains to which these techniques apply is also very broad, from vision to speech recognition, text understanding, gaming, music, etc.

A non-exhaustive list of relevant topics:
  • 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

The program will include keynote presentations from invited speakers, oral presentations, and posters.

Submission of Conference Track Papers

OpenReview ICLR 2018 Conference Track

General Chairs

  • Yoshua Bengio, Université de Montreal 
  • Yann LeCun, New York University and Facebook

Senior Program Chair

  • Tara Sainath, Google

Program Chairs

  • Iain Murray, University of Edinburgh
  • Marc’Aurelio Ranzato, Facebook
  • Oriol Vinyals, Google DeepMind

Steering Committee

  • Aaron Courville, Université de Montreal
  • Hugo Larochelle, Google


The organizers can be contacted at iclr2018.programchairs@gmail.com

Important Dates:

  • Apr 30 - May 3, 2017

Registration Cancellation Policy