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