ICLR 2020
Event Rating
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List of all ratings can be found at ICLR 2020/rating
| ICLR 2020 | |
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Eighth International Conference on Learning Representations
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| Event in series | ICLR |
| Dates | 2020/04/26 (iCal) - 2020/04/30 |
| Homepage: | https://iclr.cc/Conferences/2020/ |
| Twitter account: | @ICLR_conf_ |
| Submitting link: | https://openreview.net/group?id=ICLR.cc/2020/Conference |
| Location | |
| Location: | Addis Ababa, ET/AA, Ethiopia |
| Important dates | |
| Submissions: | 2019/09/25 |
| Notification: | 2019/12/19 |
| Papers: | Submitted 2594 / Accepted 687 (26.5 %) |
| Committees | |
| General chairs: | Alexander Rush, Shakir Mohamed |
| PC chairs: | Dawn Song, Kyunghyun Cho, Martha White |
| Workshop chairs: | Gabriel Synnaeve, Asja Fischer |
| PC members: | Abhishek Kumar, Adam White, Aleksander Madry, Alexandra Birch |
| Table of Contents | |
| Tweets by @ICLR_conf_| colspan="2" style="padding-top: 2px; " | | |
Virtual Conference Formerly Addis Ababa ETHIOPIA
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.
TOPICS
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