The deadline for submitting papers to the 21st International Conference on Neural Information Processing (ICONIP) 2014 is May 2, 2014. This conference will be held in Kuching, Malaysia, 3-6 November, 2014.
Monday, March 24, 2014
Friday, March 21, 2014
Neural Networks new articles 10 March - 16 March
1. Learning invariant object recognition from temporal correlation in a hierarchical network
Author(s): Markus Lessmann, Rolf P. Würtz
2. Impulsive synchronization schemes of stochastic complex networks with switching topology: Average time approach
Author(s): Chaojie Li, Wenwu Yu, Tingwen Huang
Author(s): Markus Lessmann, Rolf P. Würtz
2. Impulsive synchronization schemes of stochastic complex networks with switching topology: Average time approach
Author(s): Chaojie Li, Wenwu Yu, Tingwen Huang
Thursday, March 20, 2014
Finding an academic job
Finding a job for any profession is difficult, but finding a job in
academia can be ridiculously hard. Since I finished my PhD ten years
ago, I've had two periods of unemployment, totalling more than six
months out of work. Since I was the sole income-earner for my family at
the time, those periods were particularly difficult and stressful to get
through, but I got through them, as much by luck as by design.
I've come across a collection of articles on finding academic jobs, that I wanted to share and comment on. I tend to agree with what most of them have to say, even though they're not specific to computational intelligence in particular or even computer science or engineering in general.
The first discusses whether you should even go for an academic job. For computer scientists it is probably easier to go into industry with a PhD than it is for other PhD graduates. The tech industry is always strong somewhere, and it is always growing, so there are always jobs to be had. In my own country of New Zealand, at any one time there are usually between two and three thousand vacancies in the IT sector, and that's out of a country of around four million.
The second article discusses ways to improve your chances of getting an academic job. The authors mention engaging with the community, publishing papers and emphasising transferable skills. I've actually had one supervisor tell me that there is nothing more important than publishing papers, while this article argues that too much publishing runs the risk of establishing nothing more than an unfocussed research record, and this article argues that teaching experience, including experience designing and administering courses, is very important for getting an academic job. Even though I was more interested in the research side of things during my Honours year, I still worked as a tutor for a database course, and during my post-grad years I tutored computational intelligence courses. Near the end of my studies, I worked full-time as a teaching fellow (I believe in the USA that would be Teaching Assistant, but I can't be sure) and did re-work and administer courses. I am quite certain that this experience helped me to get the job I have now.
Having decided to stay in academia, and done the ground-work to enter the academic profession, the next step is to find an academic job. When you've identified a job you are interested in, the first thing you must do it get your academic CV in order. There are several common mistakes you must avoid in this document, since one mistake is all it takes for a recruiter (who are not academics) to discard your entire application. The previous two articles have some very good pieces of advice for laying out your CV, and I have applied several of them to improve my own CV.
Things like career objective statements should also be left out of a CV. My current job means that I regularly receive unsolicited emails complete with CV from people wanting a teaching job in my department, and most of them have things like career objective statements that have nothing whatsoever to do with my department or any kind of teaching job. Nothing says "desperate blanket bombing" like failing to do even a minimum of research about the place you are sending your job application to.
The other major component of an application for an academic job is the cover letter. This should also be specific for the position you are applying for, it should cover all of the criteria mentioned in the job advertisement, and it should be short. If you make it too long and detailed, then you run the risk of boring the recruiter before they finish reading, which usually results in your application being thrown away.
If you have a compelling CV, and have written a very good cover letter that shows that you are very well suited to the job, then you might get an interview. This article talks about how to prepare for an interview. One of the points in it that I would emphasise is the need to do your research before the interview. One of the fundamental rules in the Art of War is "Know yourself, and know others, and you shall have one hundred victories in one hundred battles". This applies to interviews as well! Know who is going to be interviewing you: have they published with anyone you know? Is there any other connection? What relationship does their research have to yours? This article also has some tips on how to handle tricky interview questions. Some questions just can't be answered well, like the question I got once about how I demonstrated an awareness of diversity in the classroom (I'm from the whitest district in New Zealand and I married a Chinese, I think that shows a pretty good awareness of diversity). Obviously, some self-confidence is very important, and I've been lucky in that a couple of times some really good people have boosted my self-esteem just before interviews.
Usually, by the end of an interview, I know whether I've gotten the job or not, just by the way the interview went. If I have struggled with any of the questions, then I probably won't get it. If it's gone smoothly, then I know I've got a much better chance. There have been a couple of cases where the interview went well and I still didn't get the job, but those were years ago and for positions that were probably above my skill level at the time.
Job hunting is brutal, and academic positions invariably attract a lot of applications (especially New Zealand positions, as for some reason a lot of people want to move here). The last time I was out of work, I sent off two dozen applications, which resulted in three interviews, which led to two job offers. And that was with a PhD, four years teaching experience, almost five years post-doc experience, and more than forty publications. But, if you stick to it, you will find a job. It might not be the job you first had in mind when you started, but it will be just as good, and any job is good experience if you're clever about how you do it.
I've come across a collection of articles on finding academic jobs, that I wanted to share and comment on. I tend to agree with what most of them have to say, even though they're not specific to computational intelligence in particular or even computer science or engineering in general.
The first discusses whether you should even go for an academic job. For computer scientists it is probably easier to go into industry with a PhD than it is for other PhD graduates. The tech industry is always strong somewhere, and it is always growing, so there are always jobs to be had. In my own country of New Zealand, at any one time there are usually between two and three thousand vacancies in the IT sector, and that's out of a country of around four million.
The second article discusses ways to improve your chances of getting an academic job. The authors mention engaging with the community, publishing papers and emphasising transferable skills. I've actually had one supervisor tell me that there is nothing more important than publishing papers, while this article argues that too much publishing runs the risk of establishing nothing more than an unfocussed research record, and this article argues that teaching experience, including experience designing and administering courses, is very important for getting an academic job. Even though I was more interested in the research side of things during my Honours year, I still worked as a tutor for a database course, and during my post-grad years I tutored computational intelligence courses. Near the end of my studies, I worked full-time as a teaching fellow (I believe in the USA that would be Teaching Assistant, but I can't be sure) and did re-work and administer courses. I am quite certain that this experience helped me to get the job I have now.
Having decided to stay in academia, and done the ground-work to enter the academic profession, the next step is to find an academic job. When you've identified a job you are interested in, the first thing you must do it get your academic CV in order. There are several common mistakes you must avoid in this document, since one mistake is all it takes for a recruiter (who are not academics) to discard your entire application. The previous two articles have some very good pieces of advice for laying out your CV, and I have applied several of them to improve my own CV.
Things like career objective statements should also be left out of a CV. My current job means that I regularly receive unsolicited emails complete with CV from people wanting a teaching job in my department, and most of them have things like career objective statements that have nothing whatsoever to do with my department or any kind of teaching job. Nothing says "desperate blanket bombing" like failing to do even a minimum of research about the place you are sending your job application to.
The other major component of an application for an academic job is the cover letter. This should also be specific for the position you are applying for, it should cover all of the criteria mentioned in the job advertisement, and it should be short. If you make it too long and detailed, then you run the risk of boring the recruiter before they finish reading, which usually results in your application being thrown away.
If you have a compelling CV, and have written a very good cover letter that shows that you are very well suited to the job, then you might get an interview. This article talks about how to prepare for an interview. One of the points in it that I would emphasise is the need to do your research before the interview. One of the fundamental rules in the Art of War is "Know yourself, and know others, and you shall have one hundred victories in one hundred battles". This applies to interviews as well! Know who is going to be interviewing you: have they published with anyone you know? Is there any other connection? What relationship does their research have to yours? This article also has some tips on how to handle tricky interview questions. Some questions just can't be answered well, like the question I got once about how I demonstrated an awareness of diversity in the classroom (I'm from the whitest district in New Zealand and I married a Chinese, I think that shows a pretty good awareness of diversity). Obviously, some self-confidence is very important, and I've been lucky in that a couple of times some really good people have boosted my self-esteem just before interviews.
Usually, by the end of an interview, I know whether I've gotten the job or not, just by the way the interview went. If I have struggled with any of the questions, then I probably won't get it. If it's gone smoothly, then I know I've got a much better chance. There have been a couple of cases where the interview went well and I still didn't get the job, but those were years ago and for positions that were probably above my skill level at the time.
Job hunting is brutal, and academic positions invariably attract a lot of applications (especially New Zealand positions, as for some reason a lot of people want to move here). The last time I was out of work, I sent off two dozen applications, which resulted in three interviews, which led to two job offers. And that was with a PhD, four years teaching experience, almost five years post-doc experience, and more than forty publications. But, if you stick to it, you will find a job. It might not be the job you first had in mind when you started, but it will be just as good, and any job is good experience if you're clever about how you do it.
Wednesday, March 19, 2014
IEEE Transactions on Neural Networks and Learning Systems Volume 25, Number 4, April 2014
REGULAR PAPERS
1. Dynamic Uncertain Causality Graph for Knowledge Representation and Probabilistic Reasoning: Statistics Base, Matrix, and ApplicationAuthor(s): Q. Zhang, C. Dong, Y. Cui, and Z. Yang
Pages: 645-663
2. T2FELA: Type-2 Fuzzy Extreme Learning Algorithm for Fast Training of Interval Type-2 TSK Fuzzy Logic System
Author(s): Z. Deng, K.-S. Choi, L. Cao, and S. Wang
Pages: 664-676
3. Adaptive Quasi-Newton Algorithm for Source Extraction via CCA Approach
Author(s): W.-T. Zhang, S.-T. Lou, and D.-Z. Feng
Pages: 677-689
4. Lagrange Stability of Memristive Neural Networks With Discrete and Distributed Delays
Author(s): A. Wu and Z. Zeng
Pages: 690-703
5. Attractivity Analysis of Memristor-Based Cellular Neural Networks With Time-Varying Delays
Author(s): Z. Guo, J. Wang, and Z. Yan
Pages: 704-717
6. Novel Neural Control for a Class of Uncertain Pure-Feedback Systems
Author(s): Q. Shen, P. Shi, T. Zhang, and C.-C. Lim
Pages: 718-727
7. An Ordered-Patch-Based Image Classification Approach on the Image Grassmannian Manifold
Author(s): C. Xu, T. Wang, J. Gao, S. Cao, W. Tao, and F. Liu
Pages: 728-737
8. Artificial Neural Networks for Control of a Grid-Connected Rectifier/Inverter Under Disturbance, Dynamic and Power Converter Switching Conditions
Author(s): S. Li, M. Fairbank, C. Johnson, D. C. Wunsch, E. Alonso, and J. L. Proaño
Pages: 738-750
9. A Stochastic Mean Field Model for an Excitatory and Inhibitory Synaptic Drive Cortical Neuronal Network
Author(s): Q. Hui, W. M. Haddad, J. M. Bailey, and T. Hayakawa
Pages: 751-763
10. RandomBoost: Simplified Multiclass Boosting Through Randomization
Author(s): S. Paisitkriangkrai, C. Shen, Q. Shi, and A. van den Hengel
Pages: 764-779
11. A Unified Learning Framework for Single Image Super-Resolution
Author(s): J. Yu, X. Gao, D. Tao, X. Li, and K. Zhang
Pages: 780-792
12. L1-Norm Kernel Discriminant Analysis via Bayes Error Bound Optimization for Robust Feature Extraction
Author(s): W. Zheng, Z. Lin, and H. Wang
Pages: 793-805
BRIEF PAPERS
13. Online Motor Fault Detection and Diagnosis Using a Hybrid FMM-CART ModelAuthor(s): M. Seera and C. P. Lim
Pages: 806-811
14. Feature-Based Ordering Algorithm for Data Presentation of Fuzzy ARTMAP Ensembles
Author(s): T. H. Oong and N. A. M. Isa
Pages: 812-818
16. Self-Organization in Autonomous, Recurrent, Firing-Rate CrossNets With Quasi-Hebbian Plasticity
Author(s): T. J. Walls and K. K. Likharev
Pages: 819-823
17. A Recurrent Neural Network for Solving Bilevel Linear Programming Problem
Author(s): X. He, C. Li, T. Huang, C. Li, and J. Huang
Pages: 824-829
18. Local Stability Analysis of Discrete-Time, Continuous-State, Complex-Valued Recurrent Neural Networks With Inner State Feedback
Author(s): M. Mostafa, W. G. Teich, and J. Lindner
Pages: 830-842
19. Sparse Bayesian Extreme Learning Machine for Multi-classification
Author(s): J. Luo, C.-M. Vong, and P.-K. Wong
Pages: 843
Tuesday, March 18, 2014
IEEE Transactions on Autonomous Mental Development, Volume 6, Number 1, March 2014
1. An Approach to Subjective Computing: A Robot That Learns From Interaction With Humans
Author(s): P. Grüneberg and K. Suzuki
Pages: 5-18
2. LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning
Author(s): S. Franklin, T. Madl, S. D’Mello, and J. Snaider
Pages: 19-41
3. Development of First Social Referencing Skills: Emotional Interaction as a Way to Regulate Robot Behavior
Author(s): S. Boucenna, P. Gaussier, and L. Hafemeister
Pages: 42-55
4. Object Learning Through Active Exploration
Author(s): S. Ivaldi, S. M. Nguyen, N. Lyubova, A. Droniou, V. Padois, D. Filliat, P.-Y. Oudeyer, and O. Sigaud
Pages: 56-72
5. Erratum to "Modeling Cross-Modal Interactions in EarlyWord Learning"
Pages: 73
Author(s): P. Grüneberg and K. Suzuki
Pages: 5-18
2. LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning
Author(s): S. Franklin, T. Madl, S. D’Mello, and J. Snaider
Pages: 19-41
3. Development of First Social Referencing Skills: Emotional Interaction as a Way to Regulate Robot Behavior
Author(s): S. Boucenna, P. Gaussier, and L. Hafemeister
Pages: 42-55
4. Object Learning Through Active Exploration
Author(s): S. Ivaldi, S. M. Nguyen, N. Lyubova, A. Droniou, V. Padois, D. Filliat, P.-Y. Oudeyer, and O. Sigaud
Pages: 56-72
5. Erratum to "Modeling Cross-Modal Interactions in EarlyWord Learning"
Pages: 73
Monday, March 17, 2014
IEEE Transactions on Computational Intelligence and AI in Games - Volume 6, Number 1, March 2014
1. General Self-Motivation and Strategy Identification: Case Studies Based on Sokoban and Pac-Man
Author(s): T. Anthony, D. Polani, and C. L. Nehaniv
Pages: 1-17
2. Passing a Hide-and-Seek Third-Person Turing Test
Author(s): A. Cenkner, V. Bulitko, M. Spetch, E. Legge, C. G. Anderson, and M. Brown
Pages: 18-30
3. Solving the Physical Traveling Salesman Problem: Tree Search and Macro Actions
Author(s): D. Perez, E. J. Powley, D. Whitehouse, P. Rohlfshagen, S. Samothrakis, P. I. Cowling, and S. M. Lucas
Pages: 31-45
4. Two Online Learning Playout Policies in Monte Carlo Go: An Application of Win/Loss States
Author(s): J. Basaldúa, S. Stewart, J. M. Moreno-Vega, and P. D. Drake
Pages: 46-54
5. DeepQA Jeopardy! Gamification: A Machine-Learning Perspective
Author(s): A. K. Baughman, W. Chuang, K. R. Dixon, Z. Benz, and J. Basilico
Pages: 55-66
6. A Micromanagement Task Allocation System for Real-Time Strategy Games
Author(s): K. D. Rogers and A. A. Skabar
Pages: 67-77
7. Procedural Generation of Dungeons
Author(s): R. van der Linden, R. Lopes, and R. Bidarra
Pages: 78-89
Author(s): T. Anthony, D. Polani, and C. L. Nehaniv
Pages: 1-17
2. Passing a Hide-and-Seek Third-Person Turing Test
Author(s): A. Cenkner, V. Bulitko, M. Spetch, E. Legge, C. G. Anderson, and M. Brown
Pages: 18-30
3. Solving the Physical Traveling Salesman Problem: Tree Search and Macro Actions
Author(s): D. Perez, E. J. Powley, D. Whitehouse, P. Rohlfshagen, S. Samothrakis, P. I. Cowling, and S. M. Lucas
Pages: 31-45
4. Two Online Learning Playout Policies in Monte Carlo Go: An Application of Win/Loss States
Author(s): J. Basaldúa, S. Stewart, J. M. Moreno-Vega, and P. D. Drake
Pages: 46-54
5. DeepQA Jeopardy! Gamification: A Machine-Learning Perspective
Author(s): A. K. Baughman, W. Chuang, K. R. Dixon, Z. Benz, and J. Basilico
Pages: 55-66
6. A Micromanagement Task Allocation System for Real-Time Strategy Games
Author(s): K. D. Rogers and A. A. Skabar
Pages: 67-77
7. Procedural Generation of Dungeons
Author(s): R. van der Linden, R. Lopes, and R. Bidarra
Pages: 78-89
Friday, March 14, 2014
Reminder: paper submission deadline for IEEE SSCI 2014
A reminder that the deadline for submitting papers to the IEEE Symposium Series on Computational Intelligence (SSCI) 2014 is 15 June 2014. This group of symposia will be held in Orlando, Florida, 9-12 December, 2014.
Tuesday, March 11, 2014
Neural Networks new articles 4 March - 10 March
1. Stable locality sensitive discriminant analysis for image recognition
Author(s): Quanxue Gao, Jingjing Liu, Kai Cui, Hailin Zhang, Xiaogang Wang
2. Growing Neural Gas approach for obtaining homogeneous maps by restricting the insertion of new nodes
Author(s): Yuri Quintana-Pacheco, Daniel Ruiz-Fernández, Agustín Magrans-Rico
3. Global asymptotic stability analysis for delayed neural networks using a matrix-based quadratic convex approach
Author(s): Xian-Ming Zhang, Qing-Long Han
Author(s): Quanxue Gao, Jingjing Liu, Kai Cui, Hailin Zhang, Xiaogang Wang
2. Growing Neural Gas approach for obtaining homogeneous maps by restricting the insertion of new nodes
Author(s): Yuri Quintana-Pacheco, Daniel Ruiz-Fernández, Agustín Magrans-Rico
3. Global asymptotic stability analysis for delayed neural networks using a matrix-based quadratic convex approach
Author(s): Xian-Ming Zhang, Qing-Long Han
Friday, March 7, 2014
IEEE SMC 2014 Special Session: Autonomous Learning and Evolving Intelligence
Below is a call for papers for a special session in the 2014 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC). This conference will be held in San Diego, California, October 5-8, 2014.
To achieve autonomous learning, a computer develops a form of intelligence that can evolve and adapt to its surroundings. A system that learns and evolves automatically should also operate in real-time. Currently DARPA have a challenge running for robots to operate autonomously in inhospitable environments; NSF is also recognising autonomous learning as a significant topic of research; large companies such as IBM, BT etc. also have programmes in autonomic computing and related disciplines. The objective of the proposed special session is to bring together people from academia and industry to introduce papers that look at addressing some of the fundamental problems or stumbling blocks found when a computer must learn for itself and evolve to it's surroundings.
University of Lancaster, UK
Submission of a full-length paper
May 25, 2014
Acceptance/Rejection notification
July 9, 2014
Final camera-ready paper submission
Autonomous Learning and Evolving Intelligence
Synopsis
The special session is focussed on addressing autonomous learning in computational systems in a setting where the role of the human is to merely to start/stop the process and monitor it online. It is a specific branch of machine learning, where the computer system is expected to learn for itself within a dynamically evolving and challenging environment complex processes without heuristic input or prior training.To achieve autonomous learning, a computer develops a form of intelligence that can evolve and adapt to its surroundings. A system that learns and evolves automatically should also operate in real-time. Currently DARPA have a challenge running for robots to operate autonomously in inhospitable environments; NSF is also recognising autonomous learning as a significant topic of research; large companies such as IBM, BT etc. also have programmes in autonomic computing and related disciplines. The objective of the proposed special session is to bring together people from academia and industry to introduce papers that look at addressing some of the fundamental problems or stumbling blocks found when a computer must learn for itself and evolve to it's surroundings.
Indicative Topics /Areas (not limited to)
- Autonomous Learning
- Autonomous Video Analytics
- Intelligence and Adaptive Systems
- Adaptive and Self-calibrating Sensor Systems
- Autonomous Fuzzy rule-based Systems
- Anomaly Detection
- Fault Detection and Identification
- Evolving Clustering
- Evolving Classification Methods
- Adaptive Behaviour Models
- Robotic Systems
Submission details
Papers should not exceed 8 pages in length, papers over 6 pages in length are charged extra per page (up to a max of 2). Manuscript for a Special Session should NOT be submitted in duplication to any other regular or special session and should be submitted to SMC 2014 main conference online submission system on SMC 2014 conference website. All submitted papers of Special Session have to undergo the same review process (a t least two reviewers). The technical reviewers for each Special Session paper will be members of the SMC 2014 Program Committee and qualified peer-reviewers to be nominated by the Special Session organizers.Special Session organizer
Plamen AngelovUniversity of Lancaster, UK
Important Dates
April 7, 2014Submission of a full-length paper
May 25, 2014
Acceptance/Rejection notification
July 9, 2014
Final camera-ready paper submission
Thursday, March 6, 2014
Conference paper deadline: WCCS 14
The deadline for submitting abstracts to the World Congress on Complex Systems (WCCS) 2014 is 15 May, 2014. This conference will be held in Agadir, Morocco, November 10-14, 2014.
Wednesday, March 5, 2014
Neural Networks new articles 24 February - 2 March
1. An improved robust stability result for uncertain neural networks with multiple time delays
Author(s): Sabri Arik
2. Necessary and sufficient condition for multistability of neural networks evolving on a closed hypercube
Author(s): Mauro Di Marco, Mauro Forti, Massimo Grazzini, Luca Pancioni
3. Interaction of feedforward and feedback streams in visual cortex in a firing-rate model of columnar computations
Author(s): Tobias Brosch, Heiko Neumann
4. Solving the linear interval tolerance problem for weight initialization of neural networks
Author(s): S.P. Adam, D.A. Karras, G.D. Magoulas, M.N. Vrahatis
5. Further results on robustness analysis of global exponential stability of recurrent neural networks with time delays and random disturbances
Author(s): Weiwei Luo, Kai Zhong, Song Zhu, Yi Shen
Author(s): Sabri Arik
2. Necessary and sufficient condition for multistability of neural networks evolving on a closed hypercube
Author(s): Mauro Di Marco, Mauro Forti, Massimo Grazzini, Luca Pancioni
3. Interaction of feedforward and feedback streams in visual cortex in a firing-rate model of columnar computations
Author(s): Tobias Brosch, Heiko Neumann
4. Solving the linear interval tolerance problem for weight initialization of neural networks
Author(s): S.P. Adam, D.A. Karras, G.D. Magoulas, M.N. Vrahatis
5. Further results on robustness analysis of global exponential stability of recurrent neural networks with time delays and random disturbances
Author(s): Weiwei Luo, Kai Zhong, Song Zhu, Yi Shen
Tuesday, March 4, 2014
Evolving Systems Volume 5, Number 1, 2014
1. Editorial: Applications, results and future direction (EAIS 12)
Author(s): José Antonio Iglesias & Igor Škrjanc
2. A robust fuzzy adaptive law for evolving control systems
Author(s):Sašo Blažič , Igor Škrjanc & Drago Matko
3. Fault-tolerant gait learning and morphology optimization of a polymorphic walking robot
Author(s):David Johan Christensen , Jørgen Christian Larsen & Kasper Stoy
4. Elastic Adaptive Dynamics Methodology on Ontology Matching on Evolving Folksonomy Driven Environment
Author(s):Massimiliano Dal Mas
5. Dynamic learning in cognitive robotics through a procedural long term memory
Author(s):Francisco Bellas , Pilar Caamaño , Andrés Faiña & Richard J. Duro
6. Adaptive evolving strategy for dextrous robotic manipulation
Author(s):César Arismendi , David Álvarez , Santiago Garrido & Luis Moreno
Author(s): José Antonio Iglesias & Igor Škrjanc
2. A robust fuzzy adaptive law for evolving control systems
Author(s):Sašo Blažič , Igor Škrjanc & Drago Matko
3. Fault-tolerant gait learning and morphology optimization of a polymorphic walking robot
Author(s):David Johan Christensen , Jørgen Christian Larsen & Kasper Stoy
4. Elastic Adaptive Dynamics Methodology on Ontology Matching on Evolving Folksonomy Driven Environment
Author(s):Massimiliano Dal Mas
5. Dynamic learning in cognitive robotics through a procedural long term memory
Author(s):Francisco Bellas , Pilar Caamaño , Andrés Faiña & Richard J. Duro
6. Adaptive evolving strategy for dextrous robotic manipulation
Author(s):César Arismendi , David Álvarez , Santiago Garrido & Luis Moreno
Monday, March 3, 2014
Neural Networks, Volume 52, Pages 1-76, April 2014
1. Pairwise constrained concept factorization for data representation
Pages: 1-17
Author(s): Yangcheng He, Hongtao Lu, Lei Huang, Saining Xie
2. Hybrid extreme rotation forest
Pages: 33-42
Author(s): Borja Ayerdi, Manuel Graña
3. Policy oscillation is overshooting
Pages: 43-61
Author(s): Paul Wagner
4. NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data
Pages: 62-76
Author(s): Nikola K. Kasabov
5. Construction of a Boolean model of gene and protein regulatory network with memory
Pages: 18-24
Author(s): Meng Yang, Rui Li, Tianguang Chu
6. Nonsmooth finite-time stabilization of neural networks with discontinuous activations
Pages: 25-32
Author(s): Xiaoyang Liu, Ju H. Park, Nan Jiang, Jinde Cao
Pages: 1-17
Author(s): Yangcheng He, Hongtao Lu, Lei Huang, Saining Xie
2. Hybrid extreme rotation forest
Pages: 33-42
Author(s): Borja Ayerdi, Manuel Graña
3. Policy oscillation is overshooting
Pages: 43-61
Author(s): Paul Wagner
4. NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data
Pages: 62-76
Author(s): Nikola K. Kasabov
5. Construction of a Boolean model of gene and protein regulatory network with memory
Pages: 18-24
Author(s): Meng Yang, Rui Li, Tianguang Chu
6. Nonsmooth finite-time stabilization of neural networks with discontinuous activations
Pages: 25-32
Author(s): Xiaoyang Liu, Ju H. Park, Nan Jiang, Jinde Cao
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