Difference between revisions of "ICLR 2019"
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The '''International Conference on Learning Representations''' (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. | The '''International Conference on Learning Representations''' (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. | ||
Latest revision as of 19:57, 1 April 2022
| ICLR 2019 | |
|---|---|
Seventh International Conference on Learning Representations
| |
| Event in series | ICLR |
| Dates | 2019/05/06 (iCal) - 2019/05/09 |
| Homepage: | https://iclr.cc/Conferences/2019 |
| Submitting link: | https://openreview.net/group?id=ICLR.cc/2019/Conference |
| Location | |
| Location: | New Orleans, Louisiana, USA |
| Important dates | |
| Submissions: | 2018/09/27 |
| Notification: | 2018/12/22 |
| Papers: | Submitted 1591 / Accepted 500 (31.4 %) |
| Committees | |
| General chairs: | Tara Sainath, Alexander Rush |
| PC chairs: | Sergey Levine, Karen Livescu, Shakir Mohamed |
| Workshop chairs: | Been Kim, Graham Taylor |
| Table of Contents | |
The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning.
ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics. Participants at ICLR span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
TOPIC
A non-exhaustive list of relevant topics explored at the conference include:
- 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