Wednesday, 18 July 2018

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.


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/

==========================
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

===========================================================
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


Saturday, 14 July 2018

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

Tuesday, 10 July 2018

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/

Monday, 2 July 2018

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
Author(s): Chao Du; Jun Zhu; Bo Zhang
Page(s): 3084 - 3096
http://ieeexplore.ieee.org/document/7961239/

35. Discriminative Transfer Learning Using Similarities and Dissimilarities
Author(s): Ying Lu; Liming Chen; Alexandre Saidi; Emmanuel Dellandrea; Yunhong Wang
Page(s): 3097 - 3110
http://ieeexplore.ieee.org/document/7968388/

36. Discriminative Block-Diagonal Representation Learning for Image Recognition
Author(s): Zheng Zhang; Yong Xu; Ling Shao; Jian Yang
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
Author(s): Lei Liu; Jinde Cao; Cheng Qian
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
Author(s): Xichuan Zhou; Shengli Li; Fang Tang; Shengdong Hu; Zhi Lin; Lei Zhang
Page(s): 3176 - 3187
http://ieeexplore.ieee.org/document/7983385/

42. Causal Inference on Multidimensional Data Using Free Probability Theory
Author(s): Furui Liu; Lai-Wan Chan
Page(s): 3188 - 3198
http://ieeexplore.ieee.org/document/7983426/

43. Regularized Semipaired Kernel CCA for Domain Adaptation
Author(s): Siamak Mehrkanoon; Johan A. K. Suykens
Page(s): 3199 - 3213
http://ieeexplore.ieee.org/document/7999259/

44. Patch Alignment Manifold Matting
Author(s): Xuelong Li; Kang Liu; Yongsheng Dong; Dacheng Tao
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
Author(s): Yuguang Yan; Qingyao Wu; Mingkui Tan; Michael K. Ng; Huaqing Min; Ivor W. Tsang
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
Author(s): Giovanni Da San Martino; Nicolò Navarin; Alessandro Sperduti
Page(s): 3270 - 3276
http://ieeexplore.ieee.org/document/7947106/

50. Sufficient Condition for the Existence of the Compact Set in the RBF Neural Network Control
Author(s): Jiaming Zhu; Zhiqiang Cao; Tianping Zhang; Yuequan Yang; Yang Yi
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
Author(s): Yang Liu; Jinde Cao; Bowen Li; Jianquan Lu
Page(s): 3301 - 3306
http://ieeexplore.ieee.org/document/7981369/