Visible to the public Biblio

Filters: First Letter Of Last Name is W  [Clear All Filters]
A B C D E F G H I J K L M N O P Q R S T U V [W] X Y Z   [Show ALL]
W. S. Grant, J. Tanner, L. Itti.  2017.  Biologically plausible learning in neural networks with modulatory feedback. Neural Networks. 88:32-48.

Although Hebbian learning has long been a key component in understanding neural plasticity, it has not yet been successful in modeling modulatory feedback connections, which make up a significant portion of connections in the brain. We develop a new learning rule designed around the complications of learning modulatory feedback and composed of three simple concepts grounded in physiologically plausible evidence. Using border ownership as a prototypical example, we show that a Hebbian learning rule fails to properly learn modulatory connections, while our proposed rule correctly learns a stimulus-driven model. To the authors' knowledge, this is the first time a border ownership network has been learned. Additionally, we show that the rule can be used as a drop-in replacement for a Hebbian learning rule to learn a biologically consistent model of orientation selectivity, a network which lacks any modulatory connections. Our results predict that the mechanisms we use are integral for learning modulatory connections in the brain and furthermore that modulatory connections have a strong dependence on inhibition.

Wan, Yan, Kicinger, R., Subbarao, K.  2016.  Air Traffic Management. AIAA Roadmap for Intelligent Sysems in Aerospace.
Wang, Shaohui, Ayoub, Anaheed, Sokolsky, Oleg, Lee, Insup.  2012.  Runtime Verification of Traces Under Recording Uncertainty. Proceedings of the Second International Conference on Runtime Verification (RV'11). :442–456.
Wang, Wei, Yu, Nanpeng.  2016.  LMP decomposition with three-phase DCOPF for distribution system. Innovative Smart Grid Technologies-Asia (ISGT-Asia), 2016 IEEE. :1–8.
Weerakkody, Sean, Sinopoli, Bruno, Kar, Soummya, Datta, Anupam.  2016.  Information Flow for Security in Control Systems. 55th IEEE Conference on Decision and Control (CDC). :5065-5072.
Weerakkody, Sean, Ozel, Omur, Griffioen, Paul, Sinopoli, Bruno.  Submitted.  Active Detection for Exposing Intelligent Attacks in Control Systems. 1st IEEE Conference on Control Technology and Applications.
Weerakkody, Sean, Sinopoli, Bruno.  2016.  A moving target approach for identifying malicious sensors in control systems. 54th Annual Allerton Conference on Communication, Control, and Computing. :1149–1156.
Weerakkody, Sean, Ozel, Omur, Sinopoli, Bruno.  2017.  A Bernoulli-Gaussian Physical Watermark for Detecting Integrity Attacks in Control Systems. 55th Annual Allerton Conference on Communication, Control, and Computing.
Wei Wei, Kangjin Kim, Georgios Fainekos.  2016.  Extended LTLvis Motion Planning Interface. IEEE International Conference on Systems, Man, and Cybernetics.
Weichao Wang, Chuang Wang, Le Xie, WenZhan Song, Yi Pan.  2015.  Security Education for Smart Grid: Materials, Experiments, and Evaluation. Colloquium for Information Systems Security Education (CISSE).
Weicong Ding, Prakash Ishwar, Venkatesh Saligrama.  2015.  A Topic Modeling Approach to Ranking. Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, {AISTATS} 2015, San Diego, California, USA, May 9-12, 2015. 38
Weicong Ding, Prakash Ishwar, Venkatesh Saligrama.  2015.  Learning shared rankings from mixtures of noisy pairwise comparisons. 2015 {IEEE} International Conference on Acoustics, Speech and Signal Processing, {ICASSP} 2015, South Brisbane, Queensland, Australia, April 19-24, 2015. :5446–5450.
Weicong Ding, Prakash Ishwar, Venkatesh Saligrama.  2015.  Most large topic models are approximately separable. 2015 Information Theory and Applications Workshop, {ITA} 2015, San Diego, CA, USA, February 1-6, 2015. :199–203.
Weicong Ding, Mohammad H. Rohban, Prakash Ishwar, Venkatesh Saligrama.  2014.  Efficient Distributed Topic Modeling with Provable Guarantees. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, {AISTATS} 2014, Reykjavik, Iceland, April 22-25, 2014. 33:167–175.