IEEE CIS Transactions on Neural Networks and Learning Systems Outstanding Paper Award Nomination Instructions
The IEEE Computational Intelligence Society (CIS) annually recognizes outstanding papers published in the IEEE Transactions on Neural Networks and Learning Systems (TNNLS) through its TNNLS Outstanding Paper Award established in 1997. For the current round of competition, any paper published in 2017 (Volume 28) is eligible for consideration. The prize includes a $1,000 honorarium, to be split equally among co-authors, and certificates to the author and coauthors of the selected paper. Please note no self-nomination is allowed. For those who are interested in submitting a nomination for the IEEE CIS Transactions on Neural Networks and Learning Systems Outstanding Paper Award, the materials needed are the following:
• Nomination Letter with the following information:
• Nominator: name, affiliation, and email address of nominator
• Nominated Paper: full citation of the paper, authors and their affiliations, postal addresses and email addresses.
• Basis for Nomination: detailed documentation to justify the overall quality of the paper.
• Proposed Citation: provide suggestion for the complete, correct, and succinct citation.
The Awards Committee reserves the right to make any necessary change on the citation.
• Three reference letters supporting the nomination.
• Nominated paper in PDF format.
The complete nomination packet must be saved in a single pdf file containing the above information in the given order. The name of the file must be surname_of_the_first_authorTNNLS.pdf.
The deadline is April 30, 2019 (strict deadline).
The list of awarded papers is provided below for reference:
• Published in 2014: C. L. P. Chen, G. X. Wen, Y. J. Liu, and F. Y. Wang, “Adaptive Consensus Control for a Class of Nonlinear Multiagent Time-delay Systems using Neural Networks,”, Vol. 25, No. 6, pp. 1217-1226, 2014.
• Published in 2013: Cesare Alippi, Giacomo Boracchi and Manuel Roveri, “Just-in-Time Classifiers for Recurrent Concepts", Vol. 24, No. 4, 2013, pp. 620-634.
• Published in 2013: Qiang Yu, Huajin Tang, Kay Chen Tan and Haizhou Li, “Rapid Feedforward Computation by Temporal Encoding and Learning with Spiking Neurons", Vol. 24, No. 10, 2013, pp. 1539-1552.
• Published in 2011: Long Cheng, Zeng-Guang Hou, Yingzi Lin, Min Tan, Wenjun Chris Zhang, and Fangxiang Wu, “Recurrent Neural Network for Non-Smooth Convex Optimization Problems with Application to the Identification of Genetic Regulatory Networks,” Vol. 22, No. 5, May 2011, pp. 714-726.
• Published in 2010: Huaguang Zhang, Zhenwei Liu, Guang-Bin Huang, and Zhanshan Wang “Novel Weighting-Delay-Based Stability Criteria for Recurrent Neural Networks With TimeVarying Delay,” Vol. 21, No. 1, January 2010, pp. 91-106.
• Published in 2009: H. Chen, P. Tino, and X. Yao, “Probabilistic Classification Vector Machines,” Vol. 20, No. 6, June 2009, pp. 901-914.
• Published in 2008: Qingshan Liu and Jun Wang, “A One-Layer Recurrent Neural Network with a Discontinuous Hard-Limiting Activation Function for Quadratic Programming,” Vol.19, No. 4, April 2008, pp. 558-570.
• Published in 2006: Michael E. Mavroforakis and Sergios Theodoridis, “A Geometric Approach to Support Vector Machine Classification,” Vol. 17, No. 3, May 2006, pp. 671-682.
• Published in 2005: D.L. Wang, “The Time Dimension for Scene Analysis,” Vol. 16, No. 6, November 2005, pp. 1401-1426.
• Published in 2004: J. Kwok and I. Tsang, “The Pre-Image Problem in Kernel Methods,” Vol. 15, No. 6, November 2004, pp. 1517-1525.
• Published in 2003: L. Rutkowski and K. Cpalka, “Flexible Neuro-Fuzzy Systems,” Vol. 14, No. 3, May 2003, pp. 554-574.
• Published in 2003: J. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, “Face Recognition Using Kernel Direct Discriminant Analysis Algorithms,” Vol. 14, No. 1, January 2003, pp. 117-126.
• Published in 2001: M. Baglietto, T. Parisini, and R. Zoppoli, “Distributed-Information Neural Control: the Case of Dynamic Routing in Traffic Networks,” Vol. 12, No. 3, May 2001, pp. 485-502.
• Published in 2000: George N. Karystinos and Dimitris A. Pados, “On Overfitting, Generalization and Randomly Expanded Training Sets,” Vol. 11, No. 5, September 2000, pp. 1050-1057.
• Published in 1999: R. Eckhorn, “Neural Mechanisms of Scene Segmentation: Recordings from the Visual Cortex Suggest Basic Circuits for Linking Field Models,” Vol. 10, No. 3, May 1999, pp. 464-479.
• Published in 1998: Yasuo Matsuyama, “Multiple Descent Cost Competition: Restorable SelfOrganization and Multimedia Information Processing,” Vol. 9, No. 1, January 1998, pp. 106-122.