Información de la conferencia

ICLR 2027: International Conference on Learning Representations

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Día de Entrega:
2026-09-18
Fecha de Notificación:
2026-12-16
Fecha de conferencia:
2027-04-26
Ubicación:
San Francisco, California, USA
Ediciones:
CCF: A   ICORE: A*   Vistas: 163623   Seguidores: 287   Asistentes: 20

Índice Conference Partner (CP-I)

89,3 / 100
Puesto n.º 80 de 5.683 congresos · 2% superior

N.º 8 de 740 en Inteligencia artificial y aprendizaje automático

Reconocimiento académico (35%)
100
Selectividad en la revisión (20%)
77
Ediciones celebradas (20%)
75
Atención de la comunidad (10%)
90
Integridad del registro público (15%)
100

Datos utilizados: Categorías: CCF A · Tasa de aceptación: 31.2% (media de 5 ediciones registradas) · Ediciones documentadas: 15 · Investigadores que lo siguen aquí: 287 · Investigadores que abrieron esta página en los últimos 24 meses: 111

Confianza 100 %: la parte de la puntuación respaldada por datos observados y no por la línea base neutra. Cómo se calcula esta puntuación · Ver la clasificación · Versión del algoritmo 1.1 · Calculado el 2026-09-21

Solicitud de Artículos

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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Tasa de aceptación

Tasa de aceptación media: 31.7% en 9 años (2017–2025).

AñoEnviadosAceptadosAceptados(%)
202511565370832.1%
20247304225030.8%
20234956157431.8%
20223328109532.9%
2021299786028.7%
2020259468726.5%
2019159150031.4%
201898131432%
201750719839.1%

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Revistas Relacionadas

CCFNombre CompletoFactor de ImpactoEditorISSN
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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