Dear Colleagues,

Machine-learning technology powers many aspects of modern society: from web searches to content filtering on social networks to recommendations on e-commerce websites, and it is increasingly present in consumer products such as cameras and smartphones. Machine-learning systems are used to identify objects in images, transcribe speech into text, match news items, posts or products with users’ interests, and select relevant results of search. Increasingly, these applications make use of a class of techniques called deep learning.

Deep learning based RNN sequence-to-sequence models have been successful in many problems such as machine translation, speech recognition and video captioning. The performance of machine learning methods is heavily dependent on the choice of data representation (or features) on which they are applied.

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics.

Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer.

Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence.  There are cutting-edge researches in deep learning applied to NLP.

We encourage paper submissions on the following topics (but not limited to):

- Natural language understanding and knowledge representation

- Joint Semantic Segmentation, Object Detection and Scene Recognition

- Train Neural Network models that incorporate planning as part of the learning procedure

- Theoretical understanding of deep learning

- Proposing a new architecture of a network

- Improvising on the computation of a deep network; exploiting parallel computation techniques and GPU programming

- Finding new areas where such algorithms can be applied / integrated

- Applications in vision, audio, speech, natural language processing, robotics, neuroscience, or any other field

We encourage authors to submit original research articles, case studies, research, reviews, theoretical and critical perspective and viewpoint articles. Of particular interest are articles that explore new Deep Learning techniques.

Prof. Dr. Adrian David  Cheok
Dr. Sasa  Arsovski
Guest Editors

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• Speech recognition

• NLP

• Heuristic algorithms

• Gesture recognition

• Speech

• Human computer interaction

• Artificial intelligence

• Convolutional Neural Networks

• RNN Neural Networks

• Deep Learning