会议信息

DLNN 2026: International Conference on Deep Learning and Neural Networks

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截稿日期:
2026-10-08 已截稿
通知日期:
2026-11-18
会议日期:
2026-12-11
会议地点:
Wuhan, China
届数:
浏览: 4468   关注: 0   参加: 0
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会伴指数 (CP-I)

43.3 / 100
全站第 3,658 名 / 共 5,696 个会议 · 前 65%

人工智能与机器学习 第 467 / 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%)
30
社区关注 (10%)
16
资料公开度 (15%)
55

用到的输入: 有据可查的届次:2 · 过去 24 个月打开过本页的研究者:8 人

公开资料里还缺: 历年录用率 (+4.5) · 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 1.1 · 算于 2026-10-13

征稿

DLNN 2026 (International Conference on Deep Learning and Neural Networks) is an academic conference held in Wuhan, China on 2026-12-11. The paper submission deadline is 2026-10-08. Acceptance notifications are sent on 2026-11-18.

International Conference on Deep Learning and Neural Networks (DLNN) will bring together leading researchers, engineers and scientists in the domain of interest from around the world. Topics of interest for submission include, but are not limited to: Track 1: Deep Learning Neuro-Fuzzy Algorithms Evolutionary Methods Convolutional Neural Networks (CNN) Deep Hierarchical Networks (DHN) Unsupervised Feature Learning Generative Adversarial Networks (GAN) Deep Metric Learning Methods Deep Reinforcement Learning Machine Learning for Optical Communications Learning Deep Generative Models Graph Representation Learning Active Learning Agent-Based Learning Manifold Learning Multi-Task Learning Statistical Models and Learning Computational Learning Evolutionary Algorithms and Learning Fuzzy Logic-Based Learning Parallel and Distributed Learning Deep Learning for Computing and Network Platforms Deep Learning for Photonic Devices Quantum Photonics and Deep Learning ... Track 2: Neural Networks Robot Control Knowledge Engineering Artificial Intelligence Human-Computer Interaction Deep Learning Signal Processing Information Extraction Natural Language Inference Convolutional Neural Networks Optical Neural Networks and Neuromorphic Photonics Hebbian Theory Residual Neural Networks Self-Organizing Feature Maps Biological Neural Networks Cellular Neural Networks Feedforward Neural Networks Extreme Learning Machines Multilayer Perceptrons Graph Neural Networks Multi-Layer Neural Network Neural Network Hardware Radial Basis Function Networks Recurrent Neural Networks Hopfield Neural Networks ...
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