SCOPE

Deep Learning and Big Data Analytics are two major topics of data science, nowadays. Big Data has become important in practice, as many organizations have been collecting massive amounts of data that can contain useful information for business analysis and decisions, impacting existing and future technology. A key benefit of Deep Learning is the ability to process these data and extract high-level complex abstractions as data representations, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled.

Machine-learning and artificial intelligence are pervasive in most real-world applications scenarios such as computer vision, information retrieval and summarization from structured and unstructured multimodal data sources, natural language understanding and translation, and many other application domains. Deep learning approaches, leveraging on big data, are outperforming state-of-the-art more “classical” supervised and unsupervised approaches, directly learning relevant features and data representations without requiring explicit domain knowledge or human feature engineering. These approaches are currently highly important in IoT applications.

CONFERENCE AREAS

Each of these topic areas is expanded below but the sub-topics list is not exhaustive. Papers may address one or more of the listed sub-topics, although authors should not feel limited by them. Unlisted but related sub-topics are also acceptable, provided they fit in one of the following main topic areas:

1. MODELS AND ALGORITHMS
2. MACHINE LEARNING
3. BIG DATA ANALYTICS
4. COMPUTER VISION APPLICATIONS
5. NATURAL LANGUAGE UNDERSTANDING

AREA 1: MODELS AND ALGORITHMS

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

AREA 2: MACHINE LEARNING

Active Learning
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

AREA 3: BIG DATA ANALYTICS

Extracting Complex Patterns
IoT and Smart Devices
Security Threat Detection
Semantic Indexing
Data Tagging
Fast Information Retrieval
Scalability of Models
Data Integration and Fusion
High-Dimensional Data
Streaming Data
Genomics and Bioinformatics

AREA 4: COMPUTER VISION APPLICATIONS

Image Classification
Object Detection
Face Recognition
Facial Expression Analysis
Action Recognition
Human Pose Estimation
Image Retrieval
Semantic Segmentation
Deep Image Denoising

AREA 5: NATURAL LANGUAGE UNDERSTANDING

Sentiment Analysis
Mobile Text Messaging Applications
Question Answering Applications
Speech Interfaces
Language Translation
Document Summarization
Content Filtering on Social Networks
Recommender Systems