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)
    Neural Networks for Optoelectronics
    Deep Hierarchical Networks (DHN)
    Unsupervised Feature Learning
    Generative Adversarial Networks (GAN)
    Deep Metric Learning Methods
    Deep Reinforcement Learning
    Machine Learning for Optical Communications 
    Computational Photonics with Deep Learning 
    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
    Physics-informed neural networks for photonics
    Self-Organizing Feature Maps
    Biological Neural Networks
    Neural networks for terahertz photonics and optoelectronics
    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
    ...