CALL FOR PAPERS
1st International Conference on Deep Learning Theory and Applications DeLTA

website: http://www.delta.scitevents.org

July 8 - 10, 2020 [Online Streaming]


In Cooperation with: ACM SIGAI and INNS.

Proceedings will be submitted for indexation by: DBLP, Thomson Reuters, EI, SCOPUS, Semantic Scholar, Google Scholar and Microsoft Academic.


IMPORTANT DATES:

Regular Paper Submission: February 14, 2020
Authors Notification (regular papers): April 15, 2020
Final Regular Paper Submission and Registration: April 29, 2020


Position Paper Submission: March 31, 2020
Authors Notification (position papers): May 7, 2020
Final Regular Paper Submission and Registration: May 20, 2020

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 Topics:
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


DeLTA KEYNOTE LECTURE
Petia Radeva, Universitat de Barcelona, Spain

DeLTA CONFERENCE CHAIR:
Kurosh Madani, University of Paris-EST Créteil (UPEC), France

DeLTA PROGRAM CHAIR:
Ana Fred, Instituto de Telecomunicações and University of Lisbon, Portugal

PROGRAM COMMITTEE
http://www.delta.scitevents.org/ProgramCommittee.aspx

DeLTA Secretariat
Address: Avenida de S. Francisco Xavier, Lote 7 Cv. C
Tel: +351 265 520 185
Fax: +351 265 520 186
Web: http://www.delta.scitevents.org
e-mail: delta.secretariat@insticc.org