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
...