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 is currently no common venue for researchers who share a common interest in this topic. The goal of ICLR is to help fill this void.
ICLR 2014 will be a 3-day event from April 14th to April 16th 2014, in Banff, Canada. The conference will follow the recently introduced open reviewing and open publishing publication process, which is explained in further detail here: Publication Model.
Yoshua Bengio & Yann Lecun,
We are very grateful for the generous support of the following organizations: