Conference: The 10th International Conference on Deep Learning Technologies

The 10th International Conference on Deep Learning Technologies Kunming, China Deeplearning Model Machine Learning Computer Vision Security Governance AI Ethics AI Risk Assessment

The 10th International Conference on Deep Learning Technologies

July 17-19, 2026, Kunming, China

https://www.icdlt.org

Submission Deadline: March 1, 2026
Notification of Acceptance: April 1, 2026
Camera Ready Deadline: April 15, 2026
Registration Deadline: April 15, 2026
Conference Dates: July 17-19, 2026

You are invited to attend 2026 10th International Conference on Deep Learning Technologies will be held during July 17-19, 2026 in Kunming, China. It's sponsored by Kunming University of Science and Technology, China, organized by the Faculty of Information Engineering and Automation of Kunming University of Science and Technology. It's to provide a valuable opportunity for researchers, scholars and scientists to exchange their ideas face to face in Deep Learning Technologies.

We invite researchers from deep learning technologies to participate and submit their work to the program. Likewise, any work on deep learning that has a relation to any of these fields or potential for the usage in any of them is welcome. Please refer to the different submission categories under "Call for Papers" above for further details.

Track 1: Deep Learning Model and Algorithm
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
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
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
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

The accepted paper will be included into ICDLT 2026 Conference Proceedings

PUBLICATION HISTORY

Nov 29, 2025 at 11:22 PM