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ICDLA 2026: International Conference on Deep Learning and Automation

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投稿締切日:
2026-07-24
通知日:
開催日:
2026-08-03
開催地:
Guilin, China
開催回数:
1
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ICDLA
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会伴インデックス (CP-I)

38.6 / 100
全 5,693 件中 第 5,388 位 · 上位 95%

ロボティクス・制御 分野 470 件中 第 431 位 人工知能・機械学習 分野 742 件中 第 699 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%)
19
コミュニティの注目度 (10%)
21
公開情報の充実度 (15%)
35

使用した入力: 確認できる開催回数:1 · 過去 24 か月にこのページを開いた研究者:16 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 過去の開催回 (+3.0) · 最優秀論文の記録 (+2.3)
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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 1.1 · 算出日 2026-10-06

論文募集

ICDLA 2026 (International Conference on Deep Learning and Automation) is an academic conference held in Guilin, China on 2026-08-03. The paper submission deadline is 2026-07-24.

About ICDLA 2026 The 2026 Digital Intelligence Frontier Academic Week–Guilin will take place from August 3 to 8, 2026, in Guilin, China. This premier event brings together multiple international cutting-edge conferences, focusing on deep learning, automation technologies, artificial intelligence, large language models, and intelligent systems. It aims to establish a distinguished, interdisciplinary exchange platform for scholars worldwide to jointly explore the scientific frontiers and industrial future of the digital intelligence era. As one of the major conferences of the Academic Week, the 2026 International Conference on Deep Learning and Automation (ICDLA 2026) will take place on August 4, 2026. At a time when the core driving force of AI is deeply integrated with industrial transformation, deep learning is leading automation technology into a new phase where perception, decision-making, and control are integrated. From highly adaptive intelligent manufacturing systems, to clusters of intelligent robots capable of understanding complex environments, to large-scale collaborative optimization in smart cities and smart grids, the integration of deep learning and automation is reshaping traditional industrial landscapes and expanding new frontiers in scientific research and engineering applications. ICDLA 2026 aims to gather global intelligence for in-depth discussions around core topics including deep learning models and algorithms, and intelligent automation systems. The conference will provide an international, high-end platform for scholars, engineers, and industry leaders worldwide: Here, you can share your latest research findings and demonstrate academic influence; engage in face-to-face exchanges with top experts in the field to spark innovative ideas; expand international collaboration networks and promote substantive scientific partnerships; and accurately grasp disciplinary development trends to gain insights into future research directions. The topics of interest for submission include, but are not limited to: ◕ Track1: Deep Learning Models and Algorithms Recurrent Neural Networks Sparse Coding Neuro-Fuzzy Algorithms Evolutionary Computation Methods Convolutional Neural Networks Deep Hierarchical Networks Unsupervised Feature Learning Deep Boltzmann Machines Generative Adversarial Networks, Autoencoder Deep Belief Networks Meta-Learning and Deep Networks Deep Reinforcement Learning Deep Generative Model Learning Deep Kernel Learning Gaussian Processes in Machine Learning Large-Scale Model Deployment Lightweight Models Generative Automation Federated Intelligence ◕Track2: Intelligent Automation Systems Scientific Research Agents Industrial Intelligent Manufacturing Intelligent Robotics Automated Inspection Cyber-Physical Systems Unmanned Systems Process Automation Intelligent Sensing and Fusion Automated Scheduling Edge Intelligence Human-Machine Collaborative Systems Medical Automation Energy Automation Multimodal Automated Perception Intelligent Decision-Making Cross-Domain Automation Real-Time Intelligent Scheduling Trustworthy Automation Verification Edge Collaborative Automation Dynamic Scenario Automation Human-Machine Collaborative Decision-Making Publication All accepted full papers will be published in the conference proceedings and will be submitted to EI Compendex / Scopus for indexing.
最終更新:Dou Sun()

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