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ICDLT 2026: International Conference on Deep Learning Technologies

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Submission Date:
2026-05-20
Notification Date:
2026-06-20
Conference Date:
2026-07-17
Location:
Kunming, China
Years:
10
Viewed: 15647   Tracked: 4   Attend: 1

Conference Partner Index (CP-I)

49.0 / 100
Ranked #1,821 of 5,684 conferences · Top 33%

#186 of 741 in Artificial Intelligence & Machine Learning

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
65
Community attention (10%)
34
Public record completeness (15%)
35

Inputs used: Editions on record: 10 · Researchers following it here: 4 · Researchers who opened this page in the past 24 months: 10

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-23

Call For Papers

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
Last updated by Dou Sun on

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