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*The Second International Workshop on Deep and Transfer
Learning (DTL 2019) *

*in conjunction with*




*The 6th International Conference on Social Networks Analysis, Management
and Security (SNAMS-2019) <https://urldefense.proofpoint.com/v2/url?u=http-3A__emergingtechnet.org_SNAMS2019_&d=DwIBaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=FdIxIz8_v_BG-gCZfCxOAeD7fUbdfw7sDejOkHuuLog&s=tt32nN1wdeNSSrie_bx5PdBDlpGGkQkImb8Z2EDwVSc&e= > The 6th
International Conference on Internet of Things: Systems, Management and
Security (IoTSMS 2019)
<https://urldefense.proofpoint.com/v2/url?u=http-3A__emergingtechnet.org_IOTSMS2019_index.php&d=DwIBaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=FdIxIz8_v_BG-gCZfCxOAeD7fUbdfw7sDejOkHuuLog&s=91hPsw7zSHj4eLGs_WccPOiQUH9pna5yp0Pwdwvv1pA&e= > Granada, Spain. October
22-25, 2019*

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.

Proceedings of the workshops will be published will be submitted for
inclusion in the IEEE-Xplore.

*Topics of interest *
==============

We invite 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
   o Deep transfer learning for robots
   o Determining rewards for machines
   o Machine translation
   o Energy consumption issues in deep reinforcement learning
   o Deep reinforcement learning for game playing
   o Stabilize learning dynamics in deep reinforcement learning
   o Scaling up prior reinforcement learning solutions
   - Deep Transfer and multi-task learning:
   o New perspectives or theories on transfer and multi-task learning
   o Dataset bias and concept drift
   o Transfer learning and domain adaptation
   o Multi-task learning
   o Feature based approaches
   o Instance based approaches
   o Deep architectures for transfer and multi-task learning
   o Transfer across different architectures, e.g. CNN to RNN
   o Transfer across different modalities, e.g. image to text
   o Transfer across different tasks, e.g. object recognition and detection
   o Transfer from weakly labeled or noisy data, e.g. Web data
   - GANs
   - Datasets, benchmarks, and open-source packages

*Paper Submission : *
===============

Authors are requested to submit papers reporting original research results
and experience. The page limit for full papers is 6 pages. Papers should be
prepared using IEEE two-column template.

IEEE Computer Society Proceedings Author Guidelines are available at: IEEE
Guidelines Link <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.ieee.org_conferences_publishing_templates.html&d=DwIBaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=FdIxIz8_v_BG-gCZfCxOAeD7fUbdfw7sDejOkHuuLog&s=mBiWUTPayJkCaJ8l2PN92KU1Dicipk2fmw2hhCMV_3s&e= >

Papers should be submitted as PDF files via the EasyChair: EasyChair Link
<https://urldefense.proofpoint.com/v2/url?u=https-3A__easychair.org_conferences_-3Fconf-3Ddtl2019&d=DwIBaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=FdIxIz8_v_BG-gCZfCxOAeD7fUbdfw7sDejOkHuuLog&s=pUSxsb4hlNs0rfxfuqlChzUKpGVYurG9uZhKKhxs9_A&e= >

Submitted research papers may not overlap with papers that have already
been published or that are simultaneously submitted to a journal or a
conference. All papers accepted for this conference are peer-reviewed and
are to be published in the conference proceedings by the indexed by IEEE
Xplore Digital Library.

*Important Dates : *
==============

*Full Paper Submission:*  July 22, 2019
*Notification of Decision:*  August 20, 2019
*Camera-Ready and Registration :* September 5, 2019

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