会议信息

AIMLSystems 2026: International Conference on AI-ML Systems

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截稿日期:
2026-06-08 已截稿
通知日期:
2026-07-30
会议日期:
2026-10-06
会议地点:
Lecco, Italy
届数:
6
浏览: 14317   关注: 3   参加: 0
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AIMLSystems
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会伴指数 (CP-I)

46.4 / 100
全站第 2,567 名 / 共 5,759 个会议 · 前 45%
学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
52
社区关注 (10%)
31
资料公开度 (15%)
35

用到的输入: 有据可查的届次：6 · 在会伴关注它的研究者：3 人 · 过去 24 个月打开过本页的研究者：10 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 45% —— 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 1.2 · 算于 2026-10-24

征稿

AIMLSystems 2026 (International Conference on AI-ML Systems) is an academic conference held in Lecco, Italy on 2026-10-06. The paper submission deadline is 2026-06-08. Acceptance notifications are sent on 2026-07-30.

We welcome submissions presenting original research that explores the interplay between AI/ML and system engineering. Our focus includes (but is not limited to) the following pivotal topics: Scalable & Efficient AI-ML Focuses on agentic AI systems, AIOps efficiency, and distributed, federated, or decentralized learning. Topics include high-performance, robust, secure, and energy-efficient systems, as well as root-cause analysis and auto-scaling for deployments on-premise, in the cloud, or at the edge. System Architectures Advanced hardware platforms (CPU, GPU, accelerators, edge devices) enabling improved cost, performance, and power efficiency; high-performance computing for AI workloads; custom hardware co-design; and data-intensive infrastructures. Socio-Economic & Decision Systems Vertical and domain-adapted foundation models, agentic workflows, and emerging AI techniques with significant system-level implications for decision making and socio-economic modeling. Domain-Specific Solutions Advanced AI-ML methods for real-world systems including healthcare, education, governance, finance, communication, security, and computer vision. Emphasis is placed on scalability, robustness, and meeting strict operational constraints. Safe & Responsible AI Safe system design, detection of out-of-distribution data and hallucinations, and rigorous verification and testing of AI systems. Includes the analysis of risks and opportunities arising from real-world deployment.
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