9th International Conference on Deep Learning Technologies (ICDLT 2025)

The 9th International Conference on Deep Learning Technologies (ICDLT 2025) explores the latest advancements in deep learning and machine learning technologies, including novel models, algorithms, and applications across various fields like computer vision, bioinformatics, and data analytics.

Topics

• MACHINE LEARNING THEORY AND TECHNOLOGY
  • Active learning
  • Novel machine and deep learning
  • Agent-based learning
  • Incremental learning and online learning
  • Multi-task learning
  • Manifold learning
  • Case-based reasoning methods
  • Bayesian networks and applications
  • Computational learning
  • Statistical models and learning
  • Fuzzy logic-based learning
  • Evolutionary algorithms and learning
  • Clustering, classification and regression
  • Genetic optimization
  • Parallel and distributed learning
  • Neural network models and learning
  • Supervised, semi-supervised and unsupervised learning
  • Reinforcement learning
  • Deep/Machine learning based theoretical and computational models
  • Tensor Learning Deep and Machine Learning for Big Data Analytics:
  • Model-based reasoning
  • Machine learning (e.g., deep, reinforcement, statistical relational, transfer)
• DEEP LEARNING MODEL AND ALGORITHM
  • Sparse Coding
  • Recurrent Neural Network (RNN)
  • Evolutionary Methods
  • Neuro-Fuzzy Algorithms
  • Deep Hierarchical Networks (DHN)
  • Convolutional Neural Networks (CNN)
  • Unsupervised Feature Learning
  • Dimensionality Reduction
  • Generative Adversarial Networks (GAN)
  • Deep Boltzmann Machines
  • Deep Belief Networks
  • Autoencoders
  • Deep Metric Learning Methods
  • Meta-Learning and Deep Networks
  • Deep Reinforcement Learning
  • MAP Inference in Deep Networks
  • Deep Kernel Learning
  • Learning Deep Generative Models
  • Gaussian Processes for Machine Learning
  • Graph Representation Learning
  • Classification Explainability
  • Clustering, Classification and Regression
• DEEP AND MACHINE LEARNING APPLICATIONS
  • Recommender systems
  • Deep Learning for Computing and Network Platforms
  • Deep Learning in Computer Vision
  • Deep Learning for Social media and networks
  • Deep Learning in Nature Language Processing
  • Deep learning in speech recognition
  • Deep learning in bioinformatics
  • Deep Learning in Machine Translation
  • Deep Learning in Climate Science
  • Deep Learning in Medical Image Analysis
  • Deep and Machine Learning for Data Mining and Knowledge
  • Deep Learning in Board Game Programs