Conference Information

DLNN 2026: International Conference on Deep Learning and Neural Networks

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Submission Date:
2026-10-08 Due in 40 days
Notification Date:
2026-11-18
Conference Date:
2026-12-11
Location:
Wuhan, China
Years:
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Call For Papers

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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