Call for Participation: 2018 IEEE International Conference on Data Science and Advanced Analytics (DSAA'2018) 1-4 Oct

Turin, Italy
https://dsaa2018.isi.it/

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Highlights:
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* 5 Keynote Speakers:
        - Christopher Bishop (Microsoft Research)
        - Francesca Dominici (Harvard University)
        - Deirdre Mulligan (UC Berkeley)
        - Claudia Perlich (NYU and TwoSigma)
        - Alessandro Vespignani (Northeastern University)

* One full-day Industrial Forum with only keynotes and invited industrial speakers.
https://dsaa2018.isi.it/program/industrial-forum

Preliminary list of Industrial Forum speakers includes:
        - Ricardo Baeza-Yates (NTENT)
        - V R Ferose (SAP)
        - Margaret Mitchell (Google)
        - Foster Provost (NYU and Detectica)
        - Mario Rasetti (ISI Foundation)
        - more to be announced!

* 5 thematic Special Sessions: (https://dsaa2018.isi.it/program/special-sessions)
        - Data Science for Social Good
        - Data Science in Computational Psychiatry and Psychiatric Research
        - EnGeoData'2018: Environmental and Geo-spatial Data Analytics
        - Opportunities and Risks for Data Science in Organizations: Banking, Finance, and Policy
        - SeCredISData – Sentiment, Emotion, and Credibility of Information in Social Data

* Keynotes of the Special Sessions, including:
        - Živko Krstić (Crossing technologies)
        - Fionn Murtagh (University of Huddersfield)
        - Paolo Rosso (Universitat Politècnica de València)
        - Marcel Salathé (EPFL)

* 7 Tutorials:
 - Project Management for Data Science
 - Deep Learning for Computer Vision: A practitioner’s viewpoint
 - Data Science Workflows Using R and Spark
 - Kernel Methods for Machine Learning and Data Science
 - NDlib:  Modelling and Analyzing Diffusion Processes over Complex Networks
 - Reproducible research using lessons learnt from software development
 - Data Sources and Techniques to Mine Human Mobility Patterns

* Data Science Contest (https://dsaa2018.isi.it/program/data-science-contest)

* Research and Applications tracks (https://dsaa2018.isi.it/calls/call-for-papers)

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Available Calls:
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Call for Sponsors:
https://dsaa2018.isi.it/_assets/site/resources/DSAA2018_sponsorship_programs.pdf

Call for Next Generation Data Scientist Award:
http://203.170.84.89/~idawis33/dsaa/?page_id=1357

Travel Awards for Female Students:
https://dsaa2018.isi.it/calls/travel-awards-female-students

Call for IEEE CIS Student and Researcher Travel Grants:
https://dsaa2018.isi.it/calls/ieee-cis-student-and-researcher-travel-grants

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What is IEEE DSAA
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The IEEE International Conference on Data Science and Advanced Analytics (DSAA) aims to be the flagship annual meeting spanning the interdisciplinary field of Data Science. DSAA focuses on the science of data science, as well as the implications of the science for applications to industry, government, and society. From the science side, DSAA spans all of the component fields of data science, including statistics, probabilistic and mathematical modeling, machine learning, data mining and knowledge discovery, complexity science, network science, business analytics, data management, infrastructure and storage, retrieval and search, security, privacy and ethics. From the applications side, DSAA aims both to show researchers important problems and issues that are revealed by real applications, and to show practitioners and users how the science can be applied to realize value. DSAA is intended to reflect the interdisciplinary nature of data science and analytics, as an alternative to the highly specialized disciplinary conferences.

DSAA 2018 in Turin, Italy, is the 5th annual installment of the conference. This year brings the collaboration of the American Statistical Association, to complement the IEEE Computational Intelligence Society and the ACM SIGKDD. This year DSAA also adds the support of the ISI Foundation, which has worked for 35 years to break down traditional silos in the sciences of complexity and data.

Call for Participation: 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP 2018), 6-7 Sept

http://smap2018.unizar.es/

* PRESENTATION:

The 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP 2018) will take place in Zaragoza (Spain), on 6th and 7th September 2018. SMAP 2018 will be organized with the support of the Aragon Engineering Research Institute (I3A) - University of Zaragoza.

The Semantic and Social Media Adaptation and Personalization (SMAP) workshop is the evolution of the Semantic Media Adaptation and Personalization initiative, which was founded during the summer of 2006 in an effort to discuss the state of the art, recent advances and future perspectives for semantic media adaptation and personalization. However, as Social Media applications have substantially transformed the way organizations, communities, and individuals interact, we have the scopes of SMAP extended towards this new trend, seeking to bring together researchers from the social web as well as from the semantic web communities.

After twelve successful workshops starting from Athens (2006), and then London (2007), Prague (2008), San Sebastian (2009), Limassol (2010), Vigo (2011), Luxembourg (2012), Bayonne (2013), Corfu (2014), Trento (2015), Thessaloniki (2016), and Bratislava (2017), the SMAP workshop series has been consolidated as a reference event in order to discuss about the newest advances in the field.

* AIM AND TOPICS:

SMAP 2018 aims to address several issues of semantic and social multimedia technologies and their use in content creation, media adaptation and user profiling. Topics of interest include but are not limited to:

   -Content creation, annotation and modeling for semantic and social web
   -Computational intelligence for media adaptation and personalization
   -Semantics-driven indexing and retrieval of multimedia contents
   -User modeling and dynamic profiling
   -Ontologies and reasoning
   -Multimedia standards
   -Semantics-based recommender systems: theory and applications
   -Recommender systems for mobile users
   -Web adaptation methods and techniques
   -Hybrid social and semantic approaches to profiling, recommendation engines or adaptation systems
   -Social multimedia applications (livecasting, audio-video sharing)
   -Social multimedia tagging and multimedia content communities
   -User-generated content mechanisms
   -Privacy/Security issues in Social and Personalized Media Applications
   -Privacy preserving data mining and social networks
   -Content customization and adaptation
   -Semantic context modeling and extraction
   -Context-aware multimedia applications
   -Adaptive and personalized multimedia summaries
   -Multilingual content navigation
   -Intelligent personalized interfaces
   -Social and semantic media collaboration platforms (e.g. semantic wikis)
   -Social web economics and business
   -Social network aggregation
   -Social data analytics
   -Social data mining
   -Adaptive/Personalized conversational media

* LOCAL ORGANIZERS:

    Sergio Ilarri (President)
    Fernando Bobillo
    Raquel Trillo

Contact: smap2018@unizar.es

* GENERAL CHAIRS:

    Sergio Ilarri
    Fernando Bobillo
    Raquel Trillo
    Martin Lopez-Nores

* PROGRAM COMMITTEE:

Adel Alti, University of Setif, Algeria
Angelos Michalas, Technological Educational Institute of Western Macedonia, Greece
Antonio Farina, University of Corunna, Spain
Athanasios Loukopoulos, University of Thessaly, Greece
Chrisa Tsinaraki, European Union - Joint Research Center (EU - JRC)
Christos Kalloniatis, University of the Aegean, Greece
Dimitris Dranidis, CITY College, The University of Sheffield International Faculty, Greece
Evaggelos Spyrou, National Technical University of Athens, Greece
Fernando Bobillo, University of Zaragoza, Spain
Florence Sedes, Universite Paul Sabatier Toulouse III, France
Francesco Guerra, University of Modena and Reggio Emilia, Italy
Ioanna Lykourentzou, Utrecht University, Netherlands
Ioannis Karydis, Department of Informatics, Ionian University, Greece
Jan Paralic, Technical University in Kosice, Slovakia
Jaroslav Zendulka, Technical University in Brno, Czech Republic
Jose Pazos-Arias, University of Vigo, Spain
Kristina Machova, Technical University in Kosice, Slovakia
Laura Po, University of Modena and Reggio Emilia, Italy
Luisa Carpente, University of Corunna, Spain
Manolis Wallace, University of Peloponnese, Greece
Maria Bielikova, Slovak University of Technology in Bratislava, Slovakia
Marian Simko, Slovak University of Technology in Bratislava, Slovakia
Martin Lopez-Nores, University of Vigo, Spain
Michal Kompan, Slovak University of Technology in Bratislava, Slovakia
Michalis Stefanidakis, Ionian University, Greece
Nicolas Tsapatsoulis, Cyprus University of Technology, Cyprus
Panagiotis Vlamos, Ionian University, Greece
Panos Alexopoulos, Textkernel B. V., Amsterdam, The Netherlands
Petr Saloun, VSB-TU Ostrava, Czech Republic
Petros Kefalas, CITY College, The University of Sheffield International Faculty, Greece
Philippe Roose, LIUPPA, Universite de Pau et des Pays de l'Adour, France
Phivos Mylonas, Ionian University, Greece
Przemyslaw Kazienko, Wroclaw University of Technology, Poland
Ramon Hermoso, University of Zaragoza, Spain
Raquel Trillo-Lado, University of Zaragoza, Spain
Sebastien Laborie, LIUPPA, Universite de Pau et des Pays de l'Adour, France
Sergio Ilarri, University of Zaragoza, Spain
Stefanos Vrochidis, Information Technologies Institute, Greece
Tadeusz Morzy, Poznan University of Technology, Poland
Theodoros Kotsilieris, TEI Peloponesse, Greece
Thrasyvoulos Tsiatsos, Aristotle University of Thessaloniki, Greece
Tomas Horvath, Eotvos Lorand University
Yannick Naudet, Luxembourg Institute of Science and Technology, Luxembourg

IEEE CIS Webinar: Algorithms that play and design games (27th July)

Webinar Speaker: Professor Julian Togelius

Webinar Chair: Keeley Crockett

Webinar Title:  Algorithms that play and design games

Date and Time: 27th July 2018 at 16:00 BST.  (11am in New York and 15:00 GMT). Please note due to British summer time rule, the UK moves its clocks forward from Greenwich Mean Time by one hour, so BST= GMT+1.

Abstract: The race is on to develop algorithms that can play a wide variety of games as well as humans, or even better. We do this both to understand how well our algorithms can solve tasks that are designed specifically to be hard for humans to solve, and to find software that can help with game development and design through automatic testing and adaptation. After recent successes with Poker and Go, the attention is now shifting to video games such as DOOM, DoTA, and StarCraft, which provide a fresh set of challenges. Even more challenging is designing agents that can play not just a single game, but any game you give it. A different kind of challenge is that of designing algorithms that can design games, on their own or together with human designers, rather than play them. I will present several examples of how methods from the computational intelligence toolbox, including evolutionary computation, neural networks, and Monte Carlo Tree Search, can be adapted to address these formidable research challenges.

Registration link: https://attendee.gotowebinar.com/register/946965776691967491

Webinar ID: 657-392-099

Biography: Julian Togelius is an Associate Professor in the Department of Computer Science and Engineering, New York University, USA. He is also a co-founder of the game AI company modl.ai. Julian works on artificial intelligence for games and games for artificial intelligence. His current main research directions involve search-based procedural content generation in games, general video game playing, player modelling, generating games based on open data, and fair and relevant benchmarking of AI through game-based competitions. He is the Editor-in-Chief of IEEE Transactions on Games, and has been chair or program chair of several of the main conferences on AI and Games. Togelius holds a BA from Lund University, an MSc from the University of Sussex, and a PhD from the University of Essex. He has previously worked at IDSIA in Lugano and at the IT University of Copenhagen.

Call for Participation: 8th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics in Tokyo

Conference: September 16th – 20th, 2018
Early-registration period: September 2nd, 2018

http://icdl-epirob2018.ogata-lab.jp/

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Keynote Speakers
==========================

Prof. Oliver Brock (Technische Universität Berlin, Germany)
http://www.robotics.tu-berlin.de/menue/team/oliver_brock/
Title: Proposals for a Developmental AI


Prof. Kenji Doya (Okinawa Institute of Science and Technology, Japan) https://groups.oist.jp/ncu
Title: What can we learn from the brain for AI

Prof. Peter Marshall (Temple University, U.S.A.) https://liberalarts.temple.edu/academics/faculty/marshall-peter-j
Title: Embodiment and Human Development

Mr. Masahiro Fujita (Sony, Japan)
Title: AIxRobotics in Sony

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Information and Calls for Papers/Posters/Participation at Conference Workshops
===========================================================

All three workshops below will be  held Monday, September 17th, 2018
http://icdl-epirob2018.ogata-lab.jp/program/

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Understanding Developmental Disorders: From Computational Models to Assistive Technology
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Organizers: Anja Kristina Philippsen*, Yukie Nagai
http://cognitive-mirroring.org/en/events/icdlepirob2018_workshop/

Aim and scope

The mechanisms of cognitive and developmental processes in humans are still far from being understood. It remains a mystery how our brain is capable of integrating high-dimensional sensory information from various sources in order to act and interact in a highly volatile environment. The amount of processing that our brain performs on an unconscious level becomes especially noticeable if the development of these mechanisms is atypical. Subjects with developmental disorders such as autism spectrum disorder (ASD) experience various difficulties in everyday life, particularly in social interactions. The causes are assumed to lie with atypical perception as well as differences in cognitive processing during the course of development.

In order to provide assistance for people with developmental disorders, it is crucial to understand more about the underlying mechanisms of cognitive and social development. In recent years, a number of novel approaches emerged for explaining differences in cognitive processes, for instance, in terms of Bayesian inference. By replicating autistic behavior in computational models or robots, or by studying the interaction patterns of children with ASD in interaction with a robot, possible mechanisms of cognitive development can be identified and systematically evaluated. The understanding we can gain from such experiments can help to overcome difficulties in communication between people with and without such disorders.

Another pathway for providing assistance for people with developmental disorders targets at developing assistive technology directed specifically toward ASD subjects, offering them assistance in understanding and participating in social interactions, or allowing them to train and explore interaction skills in therapy with a robot.

This workshop focuses on these two ways of how to assist people with developmental disorders, and discusses what we can learn from these studies about cognitive development in general. To connect and reflect these ideas, insights from developmental psychology, cognitive sciences, robotics and computational modeling are taken into account, as well as the perspective of people with ASD themselves (“Tojisha-Kenkyu”).

We invite the submission of 2-page paper abstracts which will be presented during the poster session.

Submission opens in mid-June 2018.

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Active vision, Attention, and Learning
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Organizers: Chen Yu*, David Crandall


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Continual Unsupervised Sensorimotor Learning
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Organizers: Nicolás Navarro-Guerrero*, Sao Mai Nguyen, Erhan Oztop, Junpei Zhong
https://conferences.au.dk/icdl-epirob-2018-workshop/

Aim and Scope

As the algorithms for learning single tasks in restricted environments are improving, new challenges have gained relevance. They include multi-task learning, multimodal sensorimotor learning and lifelong adaptation to injury, growth and ageing.

In this workshop we will discuss the developmental processes involved in the emergence of representations of action and perception in humans and artificial agents in continual learning. These processes include action-perception cycle, active perception, continual sensory-motor learning, environmental-driven scaffolding, and intrinsic motivation.

The discussion will be strongly motivated by behavioural and neural data. We hope to provide a discussion friendly environment to connect with research with similar interest regardless of their area of expertise which could include robotics, computer science, psychology, neuroscience, etc. We would also like to devise a roadmap or strategies to develop mathematical and computational models to improve robot performance and/or to attempt to unveil the underlying mechanisms that lead to continual adaptation to changing environment or embodiment and continual learning in open-ended environments.

Short paper (max 4 pages)
Paper submission deadline: 29th July 2018

=== Committee ===


Tetsuya Ogata (Waseda University, Japan)

Angelo Cangelosi (Plymouth University, UK)

Tadahiro Taniguchi (Ritsumeikan University, Japan)

Emre Ugur (Bogazici University, Turkey)

Junko Kanero (Koç University, Turkey)

Erhan Oztop (Özyeğin University, Turkey)

Minoru Asada (Osaka University, Japan)

Giulio Sandini (Italian Institute of Technology, Italy)

Alessandra Sciutti (Italian Institute of Technology, Italy)

Philippe Gaussier (University of Cergy-Pontoise, France)

Hiroki Mori (Waseda University, Japan)

Alexandre Pitti (University of Cergy-Pontoise, France)

Umay Suanda (University of Connecticut, USA)

Shingo Shimoda (Riken, Brain Science Institute, Japan)

Tetsunari Inamura (NII, Japan)

Hiromi Mochiyama (Tsukuba University, Japan)

Takato Horii (The University of Electro-Communications, Japan)

Shingo Murata (Waseda University, Japan)



=== Contacts ===


Alex Pitti: alexandre.pitti@u-cergy.fr
Hiroki Mori: mori@idr.ias.sci.waseda.ac.jp
Umay Suanda: s.suanda@uconn.edu

Call for submissions: 2018 IEEE CIS Webinars Competition -- Emerging Topics and Applications of Computational Intelligence

Webinar Topics in the areas of Emerging Topics and Applications of Computational Intelligence are invited.

Topics Include: Deep Learning, computational neuroscience, Brain Computer Interface, ambient intelligence, CI approaches to natural language, artificial life, cultural learning, computational intelligence for the IoT, Smart-X technologies, legal, ethical and social impacts of CI, Internet of Things, Big Data and Big Knowledge

Prizes  for each Category

1stprize -  $500 USD, 2ndprize -  $300 USD, 3rdprize  - $200 USD

Important Dates

•Opening Date: 1st  July 2018

•Closing Date: 1st  November 2018

•Announcement of Winners: Awards Ceremony at IEEE SSCI 2018, India

Competition Submission via Easy Chair<http://cis.missouri.edu/CISWebinar/EasyChair>

You will be required to submit a Webinar Title, Abstract, Category and a link to 30 minute YouTube video of your Webinar.  The webinarmust be narrated in English and be available through the entrants YouTubechannel.

How your submission will be judged ?

Submissions will be judged by a panel of CI Experts based on  the noveltyof the computational intelligence approach, soundness, relevance to emerging topics in CI, presentation andclarity. The Popularity of your webinar (number of “likes” vs “dislikes” and comments in YouTube) will be taken into consideration.

For more information contact:

DrKeeley Crockett 

email: K.Crockett@mmu.ac.uk 

Chair IEEE Webinars Sub-Commitee

CFP: IEEE Congress on Evolutionary Computation (CEC 2019)

The annual IEEE Congress on Evolutionary Computation is one of the leading events in the area of evolutionary computation. It covers all topics in evolutionary computation including, but not limited to the following areas:

• Artificial life
• Agent-based systems
• Artificial immune systems
• Bioinformatics and bioengineering
• Coevolution and collective behavior
• Combinatorial and numerical optimization
• Constraint and uncertainty handling
• Cognitive systems and applications
• Computational finance and economics
• Estimation of distribution algorithms
• Evolvable adaptive hardware and systems
• Evolutionary data mining
• Evolutionary design
• Evolutionary learning systems
• Evolutionary game theory
• Evolutionary multi-objective optimization
• Evolutionary scheduling
• Industrial applications of EC
• Particle Swarm Optimization
• Representation and operators

IEEE CEC 2019 is a world-class conference that brings together researchers and practitioners in the field of evolutionary computation and computational intelligence from around the globe. Technical exchanges within the research community will encompass keynote lectures, regular and special sessions, tutorials, and competitions, as well as poster presentations. In addition, participants will be treated to a series of social functions, receptions, and networking events to establish new connections and foster everlasting friendship among fellow counterparts.

Further Information: http://www.cec2019.org/

IEEE Transactions on Neural Networks and Learning Systems; Volume 29, Issue 7, July 2018.

The following articles appeared in the latest issue of IEEE Transactions on Neural Networks and Learning Systems: Volume 29, Issue 7, July 2018.

This issue published papers on convolutional neural network, metric learning, approximate dynamic programming, recurrent neural network, transfer learning, boolean network, among others. We welcome your submissions to IEEE TNNLS.

These articles can be retrieved on IEEE Xplore:
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5962385
or directly by clicking the individual paper URL below.

IEEE Transactions on Neural Networks and Learning Systems;
Volume 29, Issue 7, July 2018.


1. Driving Under the Influence (of Language)
Author(s): Daniel Paul Barrett; Scott Alan Bronikowski; Haonan Yu; Jeffrey Mark Siskind
Page(s): 2668 - 2683
http://ieeexplore.ieee.org/document/7945487/

2. Cascaded Subpatch Networks for Effective CNNs
Author(s): Xiaoheng Jiang; Yanwei Pang; Manli Sun; Xuelong Li
Page(s): 2684 - 2694
http://ieeexplore.ieee.org/document/7927455/

3. Neighborhood-Based Stopping Criterion for Contrastive Divergence
Author(s): Enrique Romero Merino; Ferran Mazzanti Castrillejo; Jordi Delgado Pin
Page(s): 2695 - 2704
http://ieeexplore.ieee.org/document/7930408/

4. Neural AILC for Error Tracking Against Arbitrary Initial Shifts
Author(s): Mingxuan Sun; Tao Wu; Lejian Chen; Guofeng Zhang
Page(s): 2705 - 2716
http://ieeexplore.ieee.org/document/7930446/

5. RankMap: A Framework for Distributed Learning From Dense Data Sets
Author(s): Azalia Mirhoseini; Eva L. Dyer; Ebrahim M. Songhori; Richard Baraniuk; Farinaz Koushanfar
Page(s): 2717 - 2730
http://ieeexplore.ieee.org/document/7930416/

6. Manifold Preserving: An Intrinsic Approach for Semisupervised Distance Metric Learning
Author(s): Shihui Ying; Zhijie Wen; Jun Shi; Yaxin Peng; Jigen Peng; Hong Qiao
Page(s): 2731 - 2742
http://ieeexplore.ieee.org/document/7931681/

7. Transductive Regression for Data With Latent Dependence Structure
Author(s): Nico Görnitz; Luiz Alberto Lima; Luiz Eduardo Varella; Klaus-Robert Müller; Shinichi Nakajima
Page(s): 2743 - 2756
http://ieeexplore.ieee.org/document/7931640/

8. Variance-Constrained State Estimation for Complex Networks With Randomly Varying Topologies
Author(s): Hongli Dong; Nan Hou; Zidong Wang; Weijian Ren
Page(s): 2757 - 2768
http://ieeexplore.ieee.org/document/7932967/

9. Stability Analysis of Continuous-Time and Discrete-Time Quaternion-Valued Neural Networks With Linear Threshold Neurons
Author(s): Xiaofeng Chen; Qiankun Song; Zhongshan Li; Zhenjiang Zhao; Yurong Liu
Page(s): 2769 - 2781
http://ieeexplore.ieee.org/document/7938721/

10. Improving Sparsity and Scalability in Regularized Nonconvex Truncated-Loss Learning Problems
Author(s): Qing Tao; Gaowei Wu; Dejun Chu
Page(s): 2782 - 2793
http://ieeexplore.ieee.org/document/7940055/

11. Policy Approximation in Policy Iteration Approximate Dynamic Programming for Discrete-Time Nonlinear Systems
Author(s): Wentao Guo; Jennie Si; Feng Liu; Shengwei Mei
Page(s): 2794 - 2807
http://ieeexplore.ieee.org/document/7940070/

12. Multilateral Telecoordinated Control of Multiple Robots With Uncertain Kinematics
Author(s): Di-Hua Zhai; Yuanqing Xia
Page(s): 2808 - 2822
http://ieeexplore.ieee.org/document/7940022/

13. A Peak Price Tracking-Based Learning System for Portfolio Selection
Author(s): Zhao-Rong Lai; Dao-Qing Dai; Chuan-Xian Ren; Ke-Kun Huang
Page(s): 2823 - 2832
http://ieeexplore.ieee.org/document/7942104/

14. Generalized Self-Organizing Maps for Automatic Determination of the Number of Clusters and Their Multiprototypes in Cluster Analysis
Author(s): Marian B. Gorzałczany; Filip Rudziński
Page(s): 2833 - 2845
http://ieeexplore.ieee.org/document/7942027/

15. Event-Triggered Distributed Approximate Optimal State and Output Control of Affine Nonlinear Interconnected Systems
Author(s): Vignesh Narayanan; Sarangapani Jagannathan
Page(s): 2846 - 2856
http://ieeexplore.ieee.org/document/7944635/

16. Online Feature Transformation Learning for Cross-Domain Object Category Recognition
Author(s): Xuesong Zhang; Yan Zhuang; Wei Wang; Witold Pedrycz
Page(s): 2857 - 2871
http://ieeexplore.ieee.org/document/7945493/

17. Improving CNN Performance Accuracies With Min–Max Objective
Author(s): Weiwei Shi; Yihong Gong; Xiaoyu Tao; Jinjun Wang; Nanning Zheng
Page(s): 2872 - 2885
http://ieeexplore.ieee.org/document/7945277/

18. Distribution-Preserving Stratified Sampling for Learning Problems
Author(s): Cristiano Cervellera; Danilo Macciò
Page(s): 2886 - 2895
http://ieeexplore.ieee.org/document/7945296/

19. Training DCNN by Combining Max-Margin, Max-Correlation Objectives, and Correntropy Loss for Multilabel Image Classification
Author(s): Weiwei Shi; Yihong Gong; Xiaoyu Tao; Nanning Zheng
Page(s): 2896 - 2908
http://ieeexplore.ieee.org/document/7947145/

20. Robust Least-Squares Support Vector Machine With Minimization of Mean and Variance of Modeling Error
Author(s): Xinjiang Lu; Wenbo Liu; Chuang Zhou; Minghui Huang
Page(s): 2909 - 2920
http://ieeexplore.ieee.org/document/7947133/

21. Joint Attributes and Event Analysis for Multimedia Event Detection
Author(s): Zhigang Ma; Xiaojun Chang; Zhongwen Xu; Nicu Sebe; Alexander G. Hauptmann
Page(s): 2921 - 2930
http://ieeexplore.ieee.org/document/7949100/

22. Aggregation Analysis for Competitive Multiagent Systems With Saddle Points via Switching Strategies
Author(s): Liying Zhu; Zhengrong Xiang
Page(s): 2931 - 2943
http://ieeexplore.ieee.org/document/7950995/

23. Learning Multimodal Parameters: A Bare-Bones Niching Differential Evolution Approach
Author(s): Yue-Jiao Gong; Jun Zhang; Yicong Zhou
Page(s): 2944 - 2959
http://ieeexplore.ieee.org/document/7954042/

24. Time-Varying System Identification Using an Ultra-Orthogonal Forward Regression and Multiwavelet Basis Functions With Applications to EEG
Author(s): Yang Li; Wei-Gang Cui; Yu-Zhu Guo; Tingwen Huang; Xiao-Feng Yang; Hua-Liang Wei
Page(s): 2960 - 2972
http://ieeexplore.ieee.org/document/7955072/

25. Neural Decomposition of Time-Series Data for Effective Generalization
Author(s): Luke B. Godfrey; Michael S. Gashler
Page(s): 2973 - 2985
http://ieeexplore.ieee.org/document/7955052/

26. Feature Selection Based on Neighborhood Discrimination Index
Author(s): Changzhong Wang; Qinghua Hu; Xizhao Wang; Degang Chen; Yuhua Qian; Zhe Dong
Page(s): 2986 - 2999
http://ieeexplore.ieee.org/document/7956261/

27. Multistability of Recurrent Neural Networks With Nonmonotonic Activation Functions and Unbounded Time-Varying Delays
Author(s): Peng Liu; Zhigang Zeng; Jun Wang
Page(s): 3000 - 3010
http://ieeexplore.ieee.org/document/7959655/

28. Optimal Triggering of Networked Control Systems
Author(s): Ali Heydari
Page(s): 3011 - 3021
http://ieeexplore.ieee.org/document/7959614/

29. Observer-Based Adaptive Fault-Tolerant Tracking Control of Nonlinear Nonstrict-Feedback Systems
Author(s): Chengwei Wu; Jianxing Liu; Yongyang Xiong; Ligang Wu
Page(s): 3022 - 3033
http://ieeexplore.ieee.org/document/7961225/

30. Joint Estimation of Multiple Conditional Gaussian Graphical Models
Author(s): Feihu Huang; Songcan Chen; Sheng-Jun Huang
Page(s): 3034 - 3046
http://ieeexplore.ieee.org/document/7961184/

31. Stability Analysis of Genetic Regulatory Networks With Switching Parameters and Time Delays
Author(s): Tingting Yu; Jianxing Liu; Yi Zeng; Xian Zhang; Qingshuang Zeng; Ligang Wu
Page(s): 3047 - 3058
http://ieeexplore.ieee.org/document/7961251/

32. Adaptive Neural Networks Prescribed Performance Control Design for Switched Interconnected Uncertain Nonlinear Systems
Author(s): Yongming Li; Shaocheng Tong
Page(s): 3059 - 3068
http://ieeexplore.ieee.org/document/7961253/

33. Robust and Efficient Boosting Method Using the Conditional Risk
Author(s): Zhi Xiao; Zhe Luo; Bo Zhong; Xin Dang
Page(s): 3069 - 3083
http://ieeexplore.ieee.org/document/7961197/

34. Learning Deep Generative Models With Doubly Stochastic Gradient MCMC
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Page(s): 3084 - 3096
http://ieeexplore.ieee.org/document/7961239/

35. Discriminative Transfer Learning Using Similarities and Dissimilarities
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Page(s): 3097 - 3110
http://ieeexplore.ieee.org/document/7968388/

36. Discriminative Block-Diagonal Representation Learning for Image Recognition
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Page(s): 3111 - 3125
http://ieeexplore.ieee.org/document/7968309/

37. Robustness to Training Disturbances in SpikeProp Learning
Author(s): Sumit Bam Shrestha; Qing Song
Page(s): 3126 - 3139
http://ieeexplore.ieee.org/document/7968359/

38. Bayesian Neighborhood Component Analysis
Author(s): Dong Wang; Xiaoyang Tan
Page(s): 3140 - 3151
http://ieeexplore.ieee.org/document/7968371/

39. p th Moment Exponential Input-to-State Stability of Delayed Recurrent Neural Networks With Markovian Switching via Vector Lyapunov Function
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Page(s): 3152 - 3163
http://ieeexplore.ieee.org/document/7970146/

40. Distributed Adaptive Finite-Time Approach for Formation–Containment Control of Networked Nonlinear Systems Under Directed Topology
Author(s): Yujuan Wang; Yongduan Song; Wei Ren
Page(s): 3164 - 3175
http://ieeexplore.ieee.org/document/7970142/

41. DANoC: An Efficient Algorithm and Hardware Codesign of Deep Neural Networks on Chip
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Page(s): 3176 - 3187
http://ieeexplore.ieee.org/document/7983385/

42. Causal Inference on Multidimensional Data Using Free Probability Theory
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Page(s): 3188 - 3198
http://ieeexplore.ieee.org/document/7983426/

43. Regularized Semipaired Kernel CCA for Domain Adaptation
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Page(s): 3199 - 3213
http://ieeexplore.ieee.org/document/7999259/

44. Patch Alignment Manifold Matting
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Page(s): 3214 - 3226
http://ieeexplore.ieee.org/document/7999271/

45. Supervised Learning Based on Temporal Coding in Spiking Neural Networks
Author(s): Hesham Mostafa
Page(s): 3227 - 3235
http://ieeexplore.ieee.org/document/7999227/

46. Multiple Structure-View Learning for Graph Classification
Author(s): Jia Wu; Shirui Pan; Xingquan Zhu; Chengqi Zhang; Philip S. Yu
Page(s): 3236 - 3251
http://ieeexplore.ieee.org/document/8047481/

47. Online Heterogeneous Transfer by Hedge Ensemble of Offline and Online Decisions
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Page(s): 3252 - 3263
http://ieeexplore.ieee.org/document/8064213/

48. Single-Input Pinning Controller Design for Reachability of Boolean Networks
Author(s): Fangfei Li; Huaicheng Yan; Hamid Reza Karimi
Page(s): 3264 - 3269
http://ieeexplore.ieee.org/document/7946159/

49. Tree-Based Kernel for Graphs With Continuous Attributes
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Page(s): 3270 - 3276
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50. Sufficient Condition for the Existence of the Compact Set in the RBF Neural Network Control
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Page(s): 3277 - 3282
http://ieeexplore.ieee.org/document/7954022/

51. Delayed Feedback Control for Stabilization of Boolean Control Networks With State Delay
Author(s): Rongjian Liu; Jianquan Lu; Yang Liu; Jinde Cao; Zheng-Guang Wu
Page(s): 3283 - 3288
http://ieeexplore.ieee.org/document/7955102/

52. Convolutional Sparse Autoencoders for Image Classification
Author(s): Wei Luo; Jun Li; Jian Yang; Wei Xu; Jian Zhang
Page(s): 3289 - 3294
http://ieeexplore.ieee.org/document/7962256/

53. An Algorithm for Finding the Most Similar Given Sized Subgraphs in Two Weighted Graphs
Author(s): Xu Yang; Hong Qiao; Zhi-Yong Liu
Page(s): 3295 - 3300
http://ieeexplore.ieee.org/document/7968361/

54. Normalization and Solvability of Dynamic-Algebraic Boolean Networks
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Page(s): 3301 - 3306
http://ieeexplore.ieee.org/document/7981369/