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ICLR 2026: International Conference on Learning Representations

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嵌入截止倒计时徽章
ICLR
截稿日期:
2025-09-19
通知日期:
2026-01-22
会议日期:
2026-04-24
会议地点:
Singapore
届数:
14
CCF: A   ICORE: A*   浏览: 154823   关注: 281   参加: 18

征稿

ICLR 2026 (International Conference on Learning Representations) is a CCF A / ICORE A* conference held in Singapore on 2026-04-24. The paper submission deadline is 2025-09-19. Acceptance notifications are sent on 2026-01-22.

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 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)
由 Dou Sun 最后更新于

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%

最佳论文

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AA*The Web ConferenceThe ACM Web Conference2026-10-112026-12-102027-05-10

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