International Conference on Learning Representations – Accepted Papers
From the conference overview:
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.
That should give you an idea of the range of data representations/features that you will encounter in the eighty (80) papers accepted for the conference.
ICLR 2016 will be held May 2-4, 2016 in the Caribe Hilton, San Juan, Puerto Rico.
Time to review How To Read A Paper!
Enjoy!
I first saw this in a tweet by Hugo Larochelle.