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1st Internat'l Conference on Deep Learning Theory And Applications (ins) AS

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Lieusaint

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77127 Lieusaint

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1st International Conference on Deep Learning Theory And Applications

About this Event

UPCOMING DEADLINES

Regular Paper Submission: February 14, 2020

Regular Paper Authors Notification: April 15, 2020

Regular Paper Camera Ready and Registration: April 29, 2020

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

1 . Models and Algorithms

2 . Machine Learning

3 . Big Data Analytics

4 . Computer Vision Applications

5 . Natural Language Understanding

CONFERENCE CHAIR

Kurosh Madani, University of Paris-EST Créteil (UPEC), France

PROGRAM CHAIR

Ana Fred, Instituto de Telecomunicações and Instituto Superior Técnico - Lisbon University, Portugal

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

KEYNOTE SPEAKERS

DeLTA 2020 will have several invited keynote speakers, who are internationally recognized experts in their areas. Their names are not yet confirmed.

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77127 Lieusaint

France

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