The integration of DL techniques could interest researchers studying the following topic areas (among others):

Track 1: Deep Learning Model and Algorithm

Track Chair: Hongping Gan, Northwestern Polytechnical University, China

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: Liangjian Deng, University of Electronic Science and Technology of China

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: Zhu Meng, Beijing University of Posts and Telecommunications, 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: