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
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 :
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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

Papers should be submitted as PDF files via the EasyChair: EasyChair Link

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 IEEE Computer Society Conference Publishing Service (CPS), and indexed by IEEE Xplore Digital Library