会议信息

ICLR 2027: International Conference on Learning Representations

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截稿日期:
2026-09-18
通知日期:
2026-12-16
会议日期:
2027-04-26
会议地点:
San Francisco, California, USA
届数:
CCF: A   ICORE: A*   浏览: 163618   关注: 287   参加: 20

会伴指数 (CP-I)

89.3 / 100
全站第 80 名 / 共 5,683 个会议 · 前 2%

人工智能与机器学习 第 8 / 740

学术认可 (35%)
100
投稿选择性 (20%)
77
会议传承 (20%)
75
社区关注 (10%)
90
资料公开度 (15%)
100

用到的输入: 收录等级:CCF A · 录用率:31.2%(有记录的 5 届的均值) · 有据可查的届次:15 · 在会伴关注它的研究者:287 人 · 过去 24 个月打开过本页的研究者:111 人

置信度 100% —— 分数中有多大比例来自实际观测到的数据,而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-09-21

征稿

ICLR 2027 (International Conference on Learning Representations) is a CCF A / ICORE A* conference held in San Francisco, California, USA on 2027-04-26. The paper submission deadline is 2026-09-18. Acceptance notifications are sent on 2026-12-16.

Subject Areas We consider a broad range of subject areas including feature learning, metric learning, compositional modeling, structured prediction, reinforcement learning, uncertainty quantification and issues regarding large-scale learning and non-convex optimization, as well as applications in vision, audio, speech, language, music, robotics, games, healthcare, biology, sustainability, economics, ethical considerations in ML, and others. A non-exhaustive list of relevant topics: unsupervised, self-supervised, semi-supervised, and supervised representation learning transfer learning, meta learning, and lifelong learning reinforcement learning representation learning for computer vision, audio, language, and other modalities metric learning, kernel learning, and sparse coding probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.) generative models causal reasoning optimization learning theory learning on graphs and other geometries & topologies societal considerations including fairness, safety, privacy visualization or interpretation of learned representations datasets and benchmarks infrastructure, software libraries, hardware, etc. neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.) applications to robotics, autonomy, planning applications to neuroscience & cognitive science applications to physical sciences (physics, chemistry, biology, etc.) general machine learning (i.e., none of the above) Submissions will be double blind: reviewers cannot see author names when conducting reviews, and authors cannot see reviewer names. Having papers on arxiv is allowed per the dual submission policy outlined in the author guidelines.
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录用率

平均录用率: 31.7% 9 年间 (2017–2025).

年份提交数录用数录用率(%)
202511565370832.1%
20247304225030.8%
20234956157431.8%
20223328109532.9%
2021299786028.7%
2020259468726.5%
2019159150031.4%
201898131432%
201750719839.1%

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相关期刊

CCF全称影响因子出版商ISSN
CEngineering Applications of Artificial Intelligence9.0Elsevier0952-1976
BPattern Recognition7.6Elsevier0031-3203
BNeural Networks7.2Elsevier0893-6080
AIEEE Transactions on Multimedia9.7IEEE1520-9210
BWorld Wide Web3.4Springer1386-145X
CFuture Generation Computer Systems5.9Elsevier0167-739X
BJournal of Parallel and Distributed Computing3.4Elsevier0743-7315
AIEEE Transactions on Computers3.8IEEE0018-9340
AIEEE Transactions on Dependable and Secure Computing7.5IEEE1545-5971
AIEEE Transactions on Parallel and Distributed Systems6.0IEEE1045-9219

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