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MLN 2026: International Conference on Machine Learning for Networking

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投稿締切日:
2026-10-15 締切済み
通知日:
2026-10-30
開催日:
2026-12-01
開催地:
Paris, France
開催回数:
閲覧: 16991   フォロー: 0   参加: 0
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会伴インデックス (CP-I)

50.0 / 100
全 5,759 件中 第 1,689 位 · 上位 30%

人工知能・機械学習 分野 751 件中 第 172 位 ネットワーク・通信 分野 874 件中 第 240 位

学術的評価 (35%) データなし — 中立の基準値 50 点として算入 —
投稿の選択性 (20%) データなし — 中立の基準値 50 点として算入 —
開催回数 (20%)
62
コミュニティの注目度 (10%)
18
公開情報の充実度 (15%)
55

使用した入力: 確認できる開催回数：9 · 過去 24 か月にこのページを開いた研究者：10 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 最優秀論文の記録 (+2.3)
主催者は会議を認証申請したうえで、このページから直接追加できます。スコアは毎晩再計算されます。このスコアを上げるには

信頼度 45% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.2 · 算出日 2026-10-24

論文募集

MLN 2026 (International Conference on Machine Learning for Networking) is an academic conference held in Paris, France on 2026-12-01. The paper submission deadline is 2026-10-15. Acceptance notifications are sent on 2026-10-30.

MLN 2026 is the 9th International Conference on Machine Learning for Networking. The conference brings together researchers, practitioners, engineers, and industry experts working at the intersection of artificial intelligence, machine learning, communication networks, and distributed systems. MLN 2026 aims to foster multidisciplinary exchanges on the design, operation, optimization, security, and evolution of intelligent networks, as well as on the networking infrastructures required to support emerging AI systems. The conference welcomes original research contributions, experimental studies, system and prototype descriptions, industry experiences, datasets, benchmarks, and survey papers. MLN 2026 will be held in Paris, France, from December 1st to December 3rd, 2026. Artificial intelligence and machine learning are transforming the way communication networks are designed, deployed, operated, secured, and optimized. At the same time, the rapid development of foundation models, generative AI, agentic systems, and large-scale distributed learning is creating new networking requirements in terms of performance, scalability, reliability, energy efficiency, privacy, and security. MLN 2026 provides an international forum for presenting and discussing advances at the intersection of machine learning and networking. The scope of the conference encompasses two complementary perspectives: AI and machine learning for networks: the use of intelligent methods to analyze, automate, optimize, secure, and manage communication and distributed systems. Networks and distributed systems for AI: the design of communication infrastructures, protocols, architectures, and resource- management mechanisms that support AI training, inference, agent collaboration, and intelligent services. MLN 2026 encourages contributions that combine methodological advances with networking applications, as well as interdisciplinary work spanning artificial intelligence, telecommunications, distributed computing, cybersecurity, operations research, control, and data science. Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal, to https://easychair.org/conferences/?conf=mln2026. The accepted papers will be published as a post-proceedings in Springer's LNCS. Lecture Notes in Computer Science (LNCS) series is indexed by the ISI Conference Proceedings Citation Index - Science (CPCI-S), included in ISI Web of Science, EI Engineering Index (Compendex and Inspec databases), ACM Digital Library, dblp, Google Scholar, Scopus, etc. Topics of interest include and are not limited to: Machine Learning and Artificial Intelligence for Networks Deep, reinforcement, graph, federated, and distributed learning Traffic prediction, anomaly detection, routing, and resource allocation Machine learning for 5G, 6G, IoT, satellite, and wireless networks Intelligent SDN, NFV, cloud, edge, and service orchestration Energy-efficient and sustainable communications Agentic AI and Autonomous Network Management Agentic AI for network operations and optimization Autonomous, zero-touch, and intent-based networks Multi-agent coordination and collaboration AI agents for control, fault detection, and remediation Human-in-the-loop automation, safety, and governance Generative AI, Foundation Models, and Network Digital Twins Large language models and foundation models for networking Generative AI for network design and management RAG, knowledge graphs, and natural-language interfaces Network digital twins and synthetic data generation Multimodal and resource-efficient models for cloud and edge Networking and Distributed Systems for AI Networks for distributed AI training and inference Data-center, cloud, edge, and high-performance AI networking Efficient federated learning and collaborative inference Joint optimization of communication, computing, and energy Architectures and protocols for AI-agent ecosystems Security, Trustworthiness, Experimentation, and Applications Machine learning for network security and resilience Adversarial AI and security of autonomous agents Explainable, trustworthy, and privacy-preserving AI Datasets, benchmarks, testbeds, and real-world deployments Intelligent applications for smart and critical systems Position papers are also welcome and should be clearly marked as such.
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関連ジャーナル

CCF正式名称インパクトファクター出版社ISSN
BMachine Learning2.9Springer0885-6125
AIEEE Transactions on Multimedia9.7IEEE1520-9210
CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340
CFuture Generation Computer Systems5.9Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.9Elsevier0167-8655
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IEEE Access3.6IEEE2169-3536

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