ICLR
E95182
ICLR (International Conference on Learning Representations) is a leading annual machine learning conference focused on deep learning and representation learning research.
All labels observed (3)
| Label | Occurrences |
|---|---|
| ICLR canonical | 11 |
| International Conference on Learning Representations | 4 |
| International Conference on Learning Representations 2016 | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T805006 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ICLR Context triple: [Wojciech Zaremba, publishedIn, ICLR]
-
A.
IEEE International Conference on Computer Vision
The IEEE International Conference on Computer Vision (ICCV) is a premier biennial research conference that showcases cutting-edge advances in computer vision and pattern recognition.
-
B.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition
The IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) is a premier annual international research conference showcasing cutting-edge advances in computer vision, machine learning, and pattern recognition.
-
C.
American Association for Artificial Intelligence
The American Association for Artificial Intelligence, now known as the Association for the Advancement of Artificial Intelligence (AAAI), is a leading scientific society dedicated to advancing research, education, and responsible use of artificial intelligence.
-
D.
IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence
The IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence is a leading professional body that advances research and standards in computer vision, pattern recognition, and machine learning within the IEEE community.
-
E.
ACM conferences
ACM conferences are scholarly meetings organized by the Association for Computing Machinery that bring together researchers and practitioners to present and discuss advances across various computing disciplines.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ICLR Target entity description: ICLR (International Conference on Learning Representations) is a leading annual machine learning conference focused on deep learning and representation learning research.
-
A.
IEEE International Conference on Computer Vision
The IEEE International Conference on Computer Vision (ICCV) is a premier biennial research conference that showcases cutting-edge advances in computer vision and pattern recognition.
-
B.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition
The IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) is a premier annual international research conference showcasing cutting-edge advances in computer vision, machine learning, and pattern recognition.
-
C.
American Association for Artificial Intelligence
The American Association for Artificial Intelligence, now known as the Association for the Advancement of Artificial Intelligence (AAAI), is a leading scientific society dedicated to advancing research, education, and responsible use of artificial intelligence.
-
D.
IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence
The IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence is a leading professional body that advances research and standards in computer vision, pattern recognition, and machine learning within the IEEE community.
-
E.
ACM conferences
ACM conferences are scholarly meetings organized by the Association for Computing Machinery that bring together researchers and practitioners to present and discuss advances across various computing disciplines.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
academic conference
ⓘ
machine learning conference ⓘ |
| accepts |
oral presentations
ⓘ
poster presentations ⓘ spotlight presentations ⓘ |
| acronym | ICLR ⓘ |
| coFounder |
Aaron Courville
ⓘ
Andrew Courville ⓘ
linked to:
Aaron Courville
Rob Fergus ⓘ Yann LeCun ⓘ Yoshua Bengio ⓘ |
| field |
artificial intelligence
ⓘ
deep learning ⓘ machine learning ⓘ representation learning ⓘ |
| focus |
deep neural networks
ⓘ
learning representations of data ⓘ optimization for deep learning ⓘ reinforcement learning ⓘ supervised learning ⓘ theoretical analysis of representation learning ⓘ unsupervised learning ⓘ |
| frequency | annual ⓘ |
| fullName |
International Conference on Learning Representations
ⓘ
linked to:
ICLR
|
| hasFormat |
main conference
ⓘ
oral sessions ⓘ poster sessions ⓘ tutorials ⓘ workshops ⓘ |
| inceptionYear | 2013 ⓘ |
| language | English ⓘ |
| ranking |
top-tier artificial intelligence conference
ⓘ
top-tier machine learning conference ⓘ |
| reviewProcess | double-blind peer review ⓘ |
| scope |
applications of deep learning
ⓘ
evaluation of learned representations ⓘ foundation models and large-scale pretraining ⓘ graph and sequence representations ⓘ interpretability of deep models ⓘ probabilistic and generative models ⓘ robustness and generalization in deep learning ⓘ scalable training methods ⓘ self-supervised learning ⓘ theory and practice of representation learning ⓘ |
| submissionType |
full research papers
ⓘ
reproducibility papers ⓘ workshop papers ⓘ |
| typicalMonth | April ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: ICLR Description of subject: ICLR (International Conference on Learning Representations) is a leading annual machine learning conference focused on deep learning and representation learning research.
Referenced by (16)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: ICLR
Prioritized Experience Replay DQN
→
publishedAt
→
International Conference on Learning Representations 2016
ⓘ
linked to: ICLR
linked to: ICLR
Adam: A Method for Stochastic Optimization
→
venue
→
International Conference on Learning Representations
ⓘ
linked to: ICLR