DTL 2020 : The Third International Workshop on Deep and Transfer Learning

  in Conferences   Posted on September 3, 2020

Conference Information

Submission Deadline Tuesday 15 Sep 2020 Proceedings indexed by :
Conference Dates Oct 19, 2020 - Oct 22, 2020
Conference Address Online, Online
Conference & Submission Link http://intelligenttech.org/DTL2020/
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Call for Papers

The Third IEEE co-sponsored International Workshop on Deep and Transfer Learning (DTL2020)

http://intelligenttech.org/DTL2020/

October 19th – 22nd, 2020 – Valencia, Spain Online Presentations!

Co-located with

International Conference on Intelligent Data Science Technologies and Applications (IDSTA2020)

All papers accepted to this workshop will be published in IEEE Xplore proceedings of IDSTA.

Call for papers

Deep learning approaches have caused tremendous advances in many areas of computer science. Deep learning is a branch of machine learning where the learning process is done using deep and complex architectures such as recurrent convolutional artificial neural networks. Many computer science applications have utilized deep learning such as computer vision, speech recognition, natural language processing, sentiment analysis, social network analysis, and robotics. The success of deep learning enabled the application of learning models such as reinforcement learning in which the learning process is only done by trial-and-error, solely from actions rewards or punishments. Deep reinforcement learning come to create systems that can learn how to adapt in the real world. As deep learning utilizes deep and complex architectures, the learning process usually is time and effort consuming and need huge labeled data sets. This inspired the introduction of transfer and multi-task learning approaches to better exploit the available data during training and adapt previously learned knowledge to emerging domains, tasks, or applications. Despite the fact that many research activities is ongoing in these areas, many challenging are still unsolved. This workshop will bring together researchers working on deep learning, working on the intersection of deep learning and reinforcement learning, and/or using transfer learning to simplify deep leaning, and it will help researchers with expertise in one of these fields to learn about the others. The workshop also aims to bridge the gap between theories and practices by providing the researchers and practitioners the opportunity to share ideas and discuss and criticize current theories and results. We invite the submission of original papers on all topics related to deep learning, deep reinforcement learning, and transfer and multi-task learning, with special interest in but not limited to:

Deep learning for innovative applications such machine translation, computational biology

Deep Learning for Natural Language Processing

Deep Learning for Recommender Systems

Deep learning for computer vision

Deep learning for systems and networks resource management

Optimization for Deep Learning

Deep Reinforcement Learning

Deep transfer learning for robots

Determining rewards for machines

Machine translation

Energy consumption issues in deep reinforcement learning

Deep reinforcement learning for game playing

Stabilize learning dynamics in deep reinforcement learning

Scaling up prior reinforcement learning solutions

Deep Transfer and multi-task learning:

New perspectives or theories on transfer and multi-task learning

Dataset bias and concept drift

Transfer learning and domain adaptation

Multi-task learning

Feature based approaches

Instance based approaches

Deep architectures for transfer and multi-task learning

Transfer across different architectures, e.g. CNN to RNN

Transfer across different modalities, e.g. image to text

Transfer across different tasks, e.g. object recognition and detection

Transfer from weakly labeled or noisy data, e.g. Web data

Datasets, benchmarks, and open-source packages

Recourse efficient deep learning

IMPORTANT DATES

Submission Due Date: September 15th, 2020 (Firm Deadline)

Notification: September 25th, 2020

Camera-ready submission: October 5th, 2020

JOURNAL SPECIAL ISSUES

Selected papers from the conference will be invited to submit an extended version to the following journal(s). Confirmed Special Issues are:

Springer Peer-to-Peer Networking and Applications (IF: 2.397)

Springer Cluster Computing (IF: 1.85)

Springer Journal of Network and System Management (IF: 1.676)

MDPI Sensors (IF: 3.031)

For any inquiries, send us an email at intelligenttechorg@gmail.com.

Best regards,

IDSTA organising committee

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