International Conference on Learning Representations
ICLR is an annual conference sponsored by the Computational and Biological Learning Society
Registration for the ICLR 2016 conference is now open:
In order for the participants to make hotel reservations they can:
Please be advised that the cutoff date for the group rate at Caribe Hilton is April 1, 2016. After this date, reservation requests and rates will be based upon availability.
It is well understood that 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 representation 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 in it topics such as deep learning and feature learning, metric learning, kernel learning, compositional models, non-linear structured prediction, and issues regarding non-convex optimization.
Despite the importance of representation learning to machine learning and to application areas such as vision, speech, audio and NLP, there was no venue for researchers who share a common interest in this topic. The goal of ICLR has been to help fill this void.
The conference follows a recently introduced open reviewing and open publishing publication process, which is explained in further detail here.
Yoshua Bengio & Yann Lecun, General Chairs