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g. In Proc. ACL, 2011. GIII. B. Coertzel and M. Ikle. Steps toward a geometry of mind. In J Schmidhuber and K Thorisson, editors, Subm.to ACI-11. Springer, 2011. CICH08. B. Goertzel, M. Ikle, I. Goertzel, and A. Heljakka. Probabilistic Logic Networks. Springer, 2008. CKD89. D. E. Goldberg, B. Korb,
, by having another agent explicitly guide it in doing the thing. For instance, teaching a dog to sit by pushing its butt to the ground. • CSDLN: (Compositional Spatiotemporal Deep Learning Network): A hierarchical pattern recognition network, in which each layer corresponds to a certain spatiotemporal granularity, the nodes on a given layer c
learning. Proc. of the IEEE International Conf. on Learning and Development, Shanghai, China., 33, 2009. Bo198. B. Bollobas. Modern Graph Theory. Springer. 1998. EFTA00624119 344 A Glossary Bos02. Nick Beetroot. Existential risks. Journal of Evolution and Technology, 9, 2002. Bos03. Nick Bostrom
, by having another agent explicitly guide it in doing the thing. For instance, teaching a dog to sit by pushing its butt to the ground. • CSDLN: (Compositional Spatiotemporal Deep Learning Network): A hierarchical pattern recognition network, in which each layer corresponds to a certain spatiotemporal granularity, the nodes on a given layer c
ve learning. Proc. of the IEEE International Conf. on Learning and Development, Shanghai, China., 33, 2009. Bol98. B. Bollobas. Modern Graph Theory. Springer, 1998. HOUSE_OVERSIGHT_013259 344 Bos02. Bos03. Bro84. BS04. Buc03. Bur62. BwWss. BZO03. BZGS06. Cal96. Car85. Car97. Cas85. Cas04. Cas07. C
hing, by having another agent explicitly guide it in doing the thing. For instance, teaching a dog to sit by pushing its butt to the ground. CSDLN: (Compositional Spatiotemporal Deep Learning Network): A hierarchical pattern recognition network, in which each layer corresponds to a certain spatiotemporal granularity, the nodes on a given layer cor
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