Chairs: Plamen P. Angelov, Lancaster University, UK p.angelov@lancaster.ac.uk Jose C. Principe, University of Florida, principe@cnel.ufl.edu . Synopsis: Deep Learning is becoming a synonym of highly precise (reaching or surpassing capabilities of a human) computational intelligence technique. Very interesting and important results were reported recently in both scientific literature and also grabbed the imagination of the wider public and industry helping propel the interest towards AI, neural networks, machine learning. It was applied mostly to solve classification problems in image processing, but also for predictive tasks in speech processing and other problems. Despite the undoubted success in achieving high precision and avoiding handcrafting in feature selection a number of issues remain unresolved, such as: i) transparency and interpretability; ii) the requirement for extremely large training data set, computational resources and time; iii) overfitting and catastrophic failures with high confidence in some cases; iv) convergence proof for the case of reinforcement learning; v) rigid structure unable to be adapted/to dynamically evolve with new samples and/or new classes; vi) repeatability of the results. Methodologically, the vast majority of the techniques of this hot and quickly developing area are based exclusively on neural networks (convolutional, belief based, etc.). Very recently publications appear where the deep learning (multi-layer) architecture with different levels of abstraction is build based on fuzzy rule-based systems or fuzzy sets are used to represent coefficients/weights in Restricted Bolzman Machines, etc. The aim of the special session is to address the bottleneck issues listed above and discuss and represent alternative and most recent methods, techniques and approaches that can help resolve these issues. The specific sub-topics that will be of interest include: Interpretable/Transparent Deep Learning Computational and time complexity/efficiency of Deep Learning Methods Repeatability of the results of Deep Learning Methods Degree of confidence in the results of Deep Learning Highly Parallelisable Deep Learning Methods Deep Learning with proven convergence Re-trainability and dynamically evolving structures/architectures for Deep Learning Ensembles of Deep Learning Classifiers Fuzzy Deep Rule-based Classifiers Self-adaptive and Self-organising Deep Learning Architectures Also applications to: Computer Vision Image Classification Robotics Remote Sensing Biology and Tomography Surveillance and Defense Industry 4.0 Assistive Technologies and Digital Health Important dates: Paper Submission Deadline: 15 January 2018 Paper Acceptance Notification Date: 15 March 2018 Final Paper Submission and Early Registration Deadline: 1st May 2018 IEEE WCCI 2018: 08-13 July 2018 Submission Guidelines: Please follow the regular submission guidelines of WCCI 2018. Please notify the chairs of your submission by sending an email to: p.angelov@lancaster.ac.uk or principe@cnel.ufl.edu. This special session is supported by the IEEE Task Force on Deep Learning and by Evolving and Adaptive Fuzzy Systems. Conference Web Site

Chairs: Davide Bacciu (bacciu@di.unipi.it ), Silvio Jamil F. Guimarães and Zenilton K. G. Patrocínio Jr. http://www.icei.pucminas.br/projetos/viplab/ijcnn-deepsm/ A key factor triggering the deep learning revolution has been its ground-breaking performance on image and video processing applications. These have been built mostly on a (multi-dimensional) raw data representation of the visual information. Multimedia content, on the other hand, calls for more articulated data representations catering for the multimodal nature of this information. These are often based on a structured representation that can capture the complexity of the contextual, semantic and geometrical relationships among the visual, phonetic and textual entities and concepts. Scope and Topics: The goal of this special session is to provide a forum for researchers working on the next generation of deep learning models for machine vision and multimedia information, which are capable of extracting and processing information in a structured representation and/or with a multimodal nature. We welcome contributions proposing innovative deep models dealing with: learning hierarchical or networked representations of multimedia information; processing of structured multimedia information under the form of sequences, labelled trees, as well as more general forms of graphs; understanding and synthesizing of multimodal data; fusion of multimodal information. This special session is meant to attract researchers from deep learning, machine vision and multimedia information communities. We aim to bring together researchers with consolidated tradition on structured data processing (such as in machine learning and NLP) with those with machine vision and multimedia processing insight, but mostly working with flat-data representations. Topics of interest for this special session include, but are not limited to, the following: deep learning models for structured data; representation learning in machine vision and multimedia processing; hierarchical/structured visual processing; deep models for visual data streams; generative and variational deep learning for multimedia data; multimedia data synthesis; attentional and bio-inspired models for the processing of visual and audio information; applied deep learning to machine vision and multimedia processing, such as: biomedical images and biobanks, pose and gesture estimation from graphs, etc.; innovative software and libraries for deep learning and multimedia content understanding. Important dates: Paper Submission Deadline: 15 January 2018 Paper Acceptance Notification Date: 15 March 2018 Final Paper Submission and Early Registration Deadline: 1st May 2018 IEEE WCCI 2018: 08-13 July 2018 This special session is supported by the IEEE Task Force on Deep Learning. Conference Web Site

Chairs: Nicolò Navarin nnavarin@math.unipd.it, Luca Oneto, Luca Pasa and Alessandro Sperduti. Description: In recent years, Deep Learning has become the go-to solution for a broad range of applications, often outperforming state-of-the-art. 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, computer vision, drug discovery, genomics and many others. Scope and Topics The goal of this special session is to provide a forum for focused discussions on new extensions of deep learning models and techniques, and to gain a deeper understanding of the difficulties and limitations associated with state-of-the-art approaches and algorithms. Practitioners should provide practical insights to the theoreticians, which in turn, should supply theoretical insights and guarantees, further strengthening and sharpening practical intuitions and wisdom. Examples of these possible extensions are: Multimodal and Multitask Deep Learning Deep Transfer Learning Deep Recurrent and Recursive Neural Networks Deep Learning on Structured Data Interpretability of Deep Learning Private and Federated Deep Learning Generative and Adversarial Deep Learning Randomized Deep Learning (Deep ELM, Deep ESN, Deep Reservoir Computing) The focus of this special session is to attract both solid contributions or preliminary results which show the potentiality and the limitations of new ideas, refinements, or contaminations between the different fields of deep learning and other fields of research in solving real world problems. Both theoretical and practical results (e.g. Social Data Analysis, Speech, Natural Language Processing, Cybersecurity) are welcome to our special session. This special session is supported by the IEEE Task Force on Deep Learning . Important dates: Paper Submission Deadline: 15 January 2018 Paper Acceptance Notification Date: 15 March 2018 Final Paper Submission and Early Registration Deadline: 1st May 2018 IEEE WCCI 2018: 08-13 July 2018 Conference Web Site