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Validation of InnoSPICE for Technology Transfer

R eferences [1] Information Technology – Process Assessment – Part 2: Performing an Assessment. International Standards Organization , ISO/IEC 15504-2, 2003. [2] Information Technology – Process Assessment – Part 5: An Exemplar Process Assessment Model. International Standards Organization , ISO/IEC 15504-5, 2006. [3] CMMI-ACQ, 2010. CMMI for Acquisition, Version 1.3. Software Engineering Institute. [4] CMMI-DEV, 2010. CMMI for Development, Version 1.3. Software Engineering Institute. [5] CMMI-SVC, 2010. CMMI for Services, Version

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Will We Hit a Wall? Forecasting Bottlenecks to Whole Brain Emulation Development

: Challenges for System Identification in Neural Circuitry. In Proceedings of the 5th International Conference on Artificial General Intelligence (AGI), 136-147. Kurzweil, R. 2012. How to Create a Mind: The Secret of Human Thought Revealed. Viking Adult. Lewis, J. S., and Niedzwiecki, R. W. 1999. Aircraft Technology and Its Relation to Emissions. In Penner, J. E.; Lister, D. H.; Griggs, D. J.; Dokken, D. J.; and McFarland, M., eds., Aviation and the Global Atmosphere. Cambridge, UK: Cambridge University Press. McNerney, J

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Further Development of Information Technology Transfer Concept for Adaptation and Exploitation of European Research Results in the Baltic Sea Region Countries

References P. Trott, Innovation Management and New Product Development , 4 th edition. Harlow, England: Pearson Education Limited, 2008. V. Varjonen, Management of Early Phases in Innovation Process: A Case Study of Commercializing Technology in a Small Enterprise. Master's Thesis, Helsinki University of Technology, 2006. M. Terziovski, Building Innovation Capability in Organizations: an International Cross-Case Perspective. London, England: Imperial College Press, 2007

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Spiking Neural Network Based on Cusp Catastrophe Theory

-Learning Digital Spiking Neuromorphic Processor in 28 nm CMOS , IEEE Transactions on Biomedical Circuits and Systems, 2018. [7] Luo Q. Fu, Y., Liu J., Qiu J. Bi, S., Cao Y., Ding X., Improving Learning Algorithm Performance for Spiking Neural Networks , 17th IEEE International Conference on Communication Technology, 2017. [8] Hodgkin A. L., Huxley A. F., A quantitative description of membrane current and its application to conduction and excitation in nerve , The Journal of Physiology, vol. 117, pp. 500-544, 1952. [9] Chang R. Hu, S., Wang H., Huang J. He, Q

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Foundations of Computing and Decision Sciences
The Journal of Poznan University of Technology
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Computable Variants of AIXI which are More Powerful than AIXItl

. Hutter, M. 2007. Universal Algorithmic Intelligence: A Mathematical Top→Down Approach. In Goertzel, B., and Pennachin, C., eds., Artificial General Intelligence , Cognitive Technologies. Berlin: Springer. 227–290. Katayama, S. 2016. Ideas for a Reinforcement Learning Algorithm that Learns Programs. In Artificial General Intelligence - 9th International Conference, AGI 2016, AGI 2016, New York, USA, July 16–19, 2016, Proceedings , 354–362. Plume, D. 1998. A Calculator for Exact Real Number Computation . Ph.D. Dissertation, University of Edinburgh

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Journal of Artificial General Intelligence
The Journal of the Artificial General Intelligence Society
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On Intra-Class Variance for Deep Learning of Classifiers

.03385. [7] Jourabloo A. and Liu X. Large-Pose Face Alignment via CNN-Based Dense 3d Model Fitting. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pages 4188–4196, Las Vegas, NV, USA, June 2016. IEEE. [8] Kingma D. P. and Ba J. Adam: A method for stochastic optimization. CoRR , abs/1412.6980, 2014. [9] Kowalski M. and Naruniec J. (Warsaw University of Technology – personal communication). [10] Lecun Y. and Cortes C. The MNIST database of handwritten digits. [11] Ren S., He K., Girshick R., and Sun J. Faster R

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Implications of Pooling Strategies in Convolutional Neural Networks: A Deep Insight

International Conference on Rough Sets and Knowledge Technology , 2014, 364-375. [54] Zeiler M. D., Fergus R., Stochastic pooling for regularization of deep convolutional neural networks, arXiv preprint arXiv:1301.3557, 2013, 1-9. [55] Zeiler M. D., Fergus R., Visualizing and understanding convolutional networks, in European conference on computer vision , 2014, 818-833. [56] Zhai S., Wu H., Kumar A., Cheng Y., Lu Y., Zhang Z., Feris R. S., S3Pool: Pooling with Stochastic Spatial Sampling, in CVPR , 2017, 4003-4011. [57] Zhou B., Khosla A

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Is Brain Emulation Dangerous?

, Neuronal Circuits of the Neocortex. Annu Rev Neurosci. 27:419-51. Drexler, E. K. 2013. Radical Abundance: How a Revolution in Nanotechnology Will Change Civilization. Public Affairs. Chapter 16. van Evera, S. 2013. Causes of War: Power and the Roots of Conflict. Cornell University Press. Floreano, D.; Mattiussi, C. 2008. Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. MIT Press. Section 3.1.3 Hawkins, J.; Blakeslee, S. 2005. On Intelligence. St. Martin's Griffin

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