Although representation learning plays a vital role in machine learning and fields including vision, voice, audio and NLP, there is still a lack of a place for scholars to exchange and share topics of concern in this field. The purpose of ICLR is to fill this gap.
The meeting will be held once a year from 20 13. It has been widely recognized by academic researchers and is considered as the top conference in the field of deep learning. Although it has not been established for a long time, it has been selected as a first-class conference by CCF.
Extended data
Features of the meeting: public review mechanism
According to the regulations, all submitted papers will be published with names and other information, and will be subject to public peer review. Any scholar can evaluate papers anonymously or under real name. After the public review, the author can also adjust and modify the paper.
OpenReview.net has completely preserved all the papers and comments before ICLR, and is also the official submission entrance of ICLR.
OpenReview.net is a public evaluation system initiated by Andrew mccallum of Amherst College of Massachusetts University. It adheres to the eight principles of public peer review, public release, open source, public discussion, public guidance, public recommendation, open API and open source, and is supported by Facebook, Google, NSF and Amherst Center of the University of Massachusetts.
In addition, papers from other academic conferences are currently accepted.