International Conference on Learning Representations
| International Conference on Learning Representations | |
|---|---|
| File:International Conference on Learning Representations.svg | |
| Abbreviation | ICLR |
| Discipline | Machine learning, artificial intelligence, feature learning |
| Publication details | |
| History | 2013–present |
| Frequency | Annual |
| yes (on openreview.net) | |
| Website | https://iclr.cc/ |
| Part of a series on |
| Machine learning and data mining |
|---|
The International Conference on Learning Representations (ICLR) is a machine learning conference typically held in late April or early May each year. Along with NeurIPS and ICML, it is one of the three primary conferences of high impact in machine learning and artificial intelligence research.[1]
The conference includes invited talks as well as oral and poster presentations of refereed papers. Since its inception in 2013, ICLR has employed an open peer review process to referee paper submissions (based on models proposed by Yann LeCun[2]). In 2019, there were 1591 paper submissions, of which 500 accepted with poster presentations (31%) and 24 with oral presentations (1.5%).[3] In 2021, there were 2997 paper submissions, of which 860 were accepted (29%).[4] It was founded by LeCun and Yoshua Bengio in 2012.[5]
Locations
[edit | edit source]- Brazil ICLR 2026, Rio de Janeiro, Brazil[6]
- Singapore ICLR 2025, Singapore
- Austria ICLR 2024, Vienna, Austria
- Rwanda ICLR 2023, Kigali, Rwanda
- ICLR 2022 (virtual conference)
- Austria ICLR 2021, Vienna, Austria (virtual conference)
- Ethiopia ICLR 2020, Addis Ababa, Ethiopia (virtual conference)[7][8]
- United States ICLR 2019, New Orleans, Louisiana, United States
- Canada ICLR 2018, Vancouver, Canada
- France ICLR 2017, Toulon, France
- United States ICLR 2016, San Juan, Puerto Rico, United States
- United States ICLR 2015, San Diego, California, United States
- Canada ICLR 2014, Banff National Park, Canada
- United States ICLR 2013, Scottsdale, Arizona, United States
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See also
[edit | edit source]References
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External links
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