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*The First International Workshop on Deep and Transfer Learning (DTL 2018) *

*https://urldefense.proofpoint.com/v2/url?u=http-3A__emergingtechnet.org_DTL2018_default.php-2A&d=DwIBaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=KuBX3HffeawIDsd-ln-rSBW6Ill69TDTG06Dp1p55zI&s=d8c5MaOxXpWPJqVyFx6y9WH6_esO0yhF-qdB3qwe6zA&e=
<https://urldefense.proofpoint.com/v2/url?u=http-3A__emergingtechnet.org_DTL2018_default.php&d=DwIBaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=KuBX3HffeawIDsd-ln-rSBW6Ill69TDTG06Dp1p55zI&s=K3Yfia5aP1ySoMGH1RivoIUyYrUcDsI9nbQWm-1T5DA&e=>

*in conjunction with*

*The Fifth International Conference on Internet of Things: Systems,
Management and Security (IoTSMS 2018)*
<https://urldefense.proofpoint.com/v2/url?u=http-3A__emergingtechnet.org_IoTSMS2018_index.php&d=DwIBaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=KuBX3HffeawIDsd-ln-rSBW6Ill69TDTG06Dp1p55zI&s=U_QiLlMQarGEUWJwHRzDdXurWg0HNTcoIHHvAkbpa-I&e=>

* Valencia, Spain. October 15-18, 2018*



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.

*Topics of interest *
==============
Authors are encouraged to submit their original work, which is not
submitted elsewhere, to this workshop. The topics of the workshop include
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

   - 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.  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-3Danlp2018&d=DwIBaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=KuBX3HffeawIDsd-ln-rSBW6Ill69TDTG06Dp1p55zI&s=DAO7V0ydQa9PmOICYvI-eCrjpNChDn0hZZXwjb64bTs&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 IoTSMS conference proceedings and will be
submitted for inclusion in IEEE Xplore Digital Library.


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

*Full Paper Submission:*  July 15, 2018
*Notification of Decision:*  August 25th, 2018
*Camera-Ready and Registration :* September 5th, 2018

All questions about submissions should be emailed to:
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