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Learning motion-difference features using Gaussian restricted Boltzmann machines for efficient human action recognition.

, , and . IJCNN, page 2123-2129. IEEE, (2014)

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Neural-Symbolic Learning and Reasoning (Dagstuhl Seminar 14381)., , , and . Dagstuhl Reports, 4 (9): 50-84 (2014)Learning to adapt requirements specifications of evolving systems., , , and . ICSE, page 856-859. ACM, (2011)Improving VG-RAM Neural Networks Performance Using Knowledge Correlation., , , , , and . ICONIP (1), volume 4232 of Lecture Notes in Computer Science, page 427-436. Springer, (2006)Neuro-symbolic Representation of Logic Programs Defining Infinite Sets., , and . ICANN (1), volume 6352 of Lecture Notes in Computer Science, page 301-304. Springer, (2010)Multi-instance learning using recurrent neural networks., and . IJCNN, page 1-6. IEEE, (2012)Neural-Symbolic Cognitive Reasoning, , and . Cognitive Technologies Springer, (2009)Neural-symbolic cognitive agents: architecture, theory and application., , , and . AAMAS, page 1621-1622. IFAAMAS/ACM, (2014)Applying the connectionist inductive learning and logic programming system to power system diagnosis., , and . ICNN, page 121-126. IEEE, (1997)Connectionist computations of intuitionistic reasoning., , and . Theor. Comput. Sci., 358 (1): 34-55 (2006)Fat-Fast VG-RAM WNN: A high performance approach., , , , , , and . Neurocomputing, (2016)