会议信息

ICDLT 2026: International Conference on Deep Learning Technologies

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截稿日期:
2026-05-20 已截稿
通知日期:
2026-06-20
会议日期:
2026-07-17
会议地点:
Kunming, China
届数:
10
浏览: 15839   关注: 4   参加: 1
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会伴指数 (CP-I)

49.0 / 100
全站第 1,887 名 / 共 5,696 个会议 · 前 34%

人工智能与机器学习 第 199 / 741

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%)
65
社区关注 (10%)
34
资料公开度 (15%)
35

用到的输入: 有据可查的届次：10 · 在会伴关注它的研究者：4 人 · 过去 24 个月打开过本页的研究者：10 人

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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 2026-10-15

征稿

ICDLT 2026 (International Conference on Deep Learning Technologies) is an academic conference held in Kunming, China on 2026-07-17. The paper submission deadline is 2026-05-20. Acceptance notifications are sent on 2026-06-20.

The integration of DL techniques could interest researchers studying the following topic areas (among others) Special Session 1: Autonomous Machine Intelligence – Theory and Applications (AMITA) (Click) Track 1: Deep Learning Model and Algorithm Track Chair: Xinhui Ma, University of Hull, United Kingdom Recurrent Neural Network (RNN) Sparse Coding Neuro-Fuzzy Algorithms Evolutionary Methods Convolutional Neural Networks (CNN) Deep Hierarchical Networks (DHN) Dimensionality Reduction Unsupervised Feature Learning Deep Boltzmann Machines Generative Adversarial Networks (GAN) Autoencoders Deep Belief Networks Meta-Learning and Deep Networks Deep Metric Learning Methods MAP Inference in Deep Networks Deep Reinforcement Learning Learning Deep Generative Models Deep Kernel Learning Graph Representation Learning Gaussian Processes for Machine Learning Clustering, Classification and Regression Classification Explainability Track 2: Machine Learning Theory and Technology Track Chair: Pascal Lorenz, University of Haute Alsace, France Novel machine and deep learning Active learning Incremental learning and online learning Agent-based learning Manifold learning Multi-task learning Bayesian networks and applications Case-based reasoning methods Statistical models and learning Computational learning Evolutionary algorithms and learning Fuzzy logic-based learning Genetic optimization Clustering, classification and regression Neural network models and learning Parallel and distributed learning Reinforcement learning Supervised, semi-supervised and unsupervised learning Tensor Learning Deep and Machine Learning for Big Data Analytics: Deep/Machine learning based theoretical and computational models Machine learning (e.g., deep, reinforcement, statistical relational, transfer) Model-based reasoning Track 3: Deep and Machine Learning Applications Track Chair: Leiming Ma, Shanghai Typhoon Institute, China Hui Zhang, Southwest University of Science and Technology, China Deep Learning for Computing and Network Platforms Recommender systems Deep Learning for Social media and networks Deep Learning in Computer Vision Deep learning in speech recognition Deep Learning in Nature Language Processing, Deep Learning in Machine Translation Deep learning in bioinformatics Deep Learning in Medical Image Analysis Deep Learning in Climate Science Deep Learning in Board Game Programs Deep and Machine Learning for Data Mining and Knowledge Track 4: Responsible AI, Security, and Governance Track Chair: Zhu Meng, Beijing University of Posts and Telecommunications, China AI safety Privacy preservation Algorithmic Fairness Fairness and ethics Classification Explainability and Explainable AI (XAI) AI Ethics AI governance Green Deep Learning Synthetic Data Generation Social and Economic Impact of AI Sustainable AI AI Risk Assessment
由 Dou Sun 最后更新于

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