ICLR 2019: International Conference on Learning Representations
- Call for paper, ranking, acceptance rate,
submission deadline, notification date, conference location, submission guidelines, and other important details
This article provides the call for paper, ranking, acceptance rate,
submission deadline, notification date, conference location, submission guidelines, and other important details of ICLR 2019: International Conference on Learning Representations all at one place.
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Conference Location
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New Orleans, Louisiana, USA
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Conference Date
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2019-04-30
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Notification Date
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Submission Deadline
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2018-09-27
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Conference Website and Submission Link
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https://iclr.cc/Conferences/2019/
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Conference Ranking
International Conference on Learning Representations ranking based on CCF, Core, and Qualis is shown below:
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CCF Ranking
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Core Ranking
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Qualis Ranking
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Click here to check the ranking of any conference.
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About CCF Ranking: The Chinese Computing Federation (CCF) Ranking provides a ranking of peer-reviewed journals and conferences in the field of computer science.
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About Core Ranking: The CORE Conference Ranking is a measure to assess the major conference in the computing field. This ranking is governed by the CORE Executive Committee. To know more about Core ranking, visit Core ranking portal.
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About Qualis Ranking: This conference ranking is published by the Brazilian ministry of education. It uses the h-index as a performance metric to rank conferences. Conferences are classified into performance groups that range from A1 (to the best), A2, B1, B2,..., B5 (to the wost). To know more about qualis ranking, visit here
Conference Acceptance Rate
Below is the acceptance rate of International Conference on Learning Representations conference for the last few years:
| Year |
Submitted Papers |
Accepted Papers |
Accepted Percentage/Acceptance Rate |
We are working hard to collect and update the acceptance rate details of the conferences for recent years. However, you can consider the above (if available) acceptance rates to predict the average chances of acceptance of your research paper at this conference.
Conference Call for paper
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 deep 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 topics such as feature learning, metric learning, compositional modeling, structured prediction, reinforcement learning, and issues regarding large-scale learning and non-convex optimization. The range of domains to which these techniques apply is also very broad, including vision, speech recognition, text understanding, games, music, computational biology, and others.A non-exhaustive list of relevant topics:
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
theoretical issues in deep learning
visualization or interpretation of learned representations
implementation issues, parallelization, software platforms, hardware
applications in vision, audio, speech, natural language processing, robotics, neuroscience, computational biology, or any other fieldThe program will include keynote presentations from invited speakers, oral presentations, and posters.Similarly to last year, submissions will be double blind, meaning that reviewers cannot see author names when performing reviews, and authors cannot see reviewer names. While we will still use OpenReview to host papers and allow for public discussions that can be seen by all, comments that are posted by reviewers will remain anonymous. If someone wants to cite a paper during the review period, OpenReview will provide a BibTeX entry that does not list the authors, but does give the title, year and url. Only at the end of the review period will the authors be revealed. While ICLR is double blind, we will not forbid authors from posting their paper on arXiv or any other public forum.Authors can revise their paper as many times as needed up to the paper submission deadline. During the review period, authors will not be allowed to revise their paper. Once the review period is over and the rebuttal period begins, authors can revise their paper but a pdfdiff will be done against the submission at the paper submission deadline. Area chairs and reviewers reserve the right to ignore changes which are significant from the original scope of the paper.
Submission Deadline
ICLR 2019: International Conference on Learning Representations submission deadline is 2018-09-27.
Note: It is generally recommended to submit your conference paper
on or before the submission deadline. Generally, conferences do not
encourage to submit the research paper after the deadline is over.
In rare scenarios, conferences extend their deadline. Decision about the
extension of the deadline is generally updated on the official conference webpage.
Notification date
Notification date of ICLR 2019: International Conference on Learning Representations is .
Note: This is the date on which conference announces the result about acceptance or rejection of submitted papers. If your research paper is accepted, the conference will request you to submit the camera ready version of your research paper by the due date. Due date to submit the camera ready version of the paper is generally posted on the official web page of the conferences or notified to you via. email.
Conference Date
ICLR 2019: International Conference on Learning Representations will start on 2019-04-30.
Note: This is the date on which the conference starts.
Conference Location
ICLR 2019: International Conference on Learning Representations will be organized at New Orleans, Louisiana, USA. This is the place where the conference is organized and the research paper is to be presented.
