Difference between revisions of "ICLR 2019"
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|Series=ICLR | |Series=ICLR | ||
|Type=Conference | |Type=Conference | ||
| − | |Field=machine learning, learning | + | |Field=machine learning, deep learning, learning |
|Start date=2019/05/06 | |Start date=2019/05/06 | ||
|End date=2019/05/09 | |End date=2019/05/09 | ||
| + | |Submission deadline=2018/09/27 | ||
|Homepage=https://iclr.cc/Conferences/2019 | |Homepage=https://iclr.cc/Conferences/2019 | ||
|City=New Orleans | |City=New Orleans | ||
|State=Louisiana | |State=Louisiana | ||
|Country=USA | |Country=USA | ||
| − | |||
|Notification=2018/12/22 | |Notification=2018/12/22 | ||
|Submitting link=https://openreview.net/group?id=ICLR.cc/2019/Conference | |Submitting link=https://openreview.net/group?id=ICLR.cc/2019/Conference | ||
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|Accepted papers=500 | |Accepted papers=500 | ||
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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. |
| + | |||
| + | 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 | ||
Latest revision as of 09:52, 12 May 2020
| 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 |
Loading map... | |
| 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 | |
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
Facts about "ICLR 2019"
| Acceptance rate | 31.4 + |
| Accepted papers | 500 + |
| Acronym | ICLR 2019 + |
| End date | May 9, 2019 + |
| Event in series | ICLR + |
| Event type | Conference + |
| Has Submitting link | https://openreview.net/group?id=ICLR.cc/2019/Conference + |
| Has coordinates | 29° 58' 34", -90° 4' 42"Latitude: 29.975997222222 Longitude: -90.078213888889 + |
| Has general chair | Tara Sainath + and Alexander Rush + |
| Has location city | New Orleans + |
| Has location country | Category:USA + |
| Has location state | Louisiana + |
| Has program chair | Sergey Levine +, Karen Livescu + and Shakir Mohamed + |
| Has workshop chair | Been Kim + and Graham Taylor + |
| Homepage | https://iclr.cc/Conferences/2019 + |
| IsA | Event + |
| Notification | December 22, 2018 + |
| Start date | May 6, 2019 + |
| Submission deadline | September 27, 2018 + |
| Submitted papers | 1,591 + |
| Title | Seventh International Conference on Learning Representations + |
