Cumincad : CUMINCAD References : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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	Kazanasmaz, T., Murat, G., And Selcen, B. (2009) Artificial Neural Networks to Predict Daylight Illuminance in Office Buildings , Building and Environment 44.8 (29), 1751-1757. Available at: https://doi.org/1.116/j.buildenv.28.11.12.

	Kecman, V. (2001) Learning and Soft Computing. Support Vector Machines , Neural Networks and Fuzzy Logic Models. The MIT Press, London

	Kennedy J, Eberhart R. (1995) Particle swarm optimization , IEEE International Conference on Neural Networks

	Kennedy, J and Eberhart, R (1995) Particle Swarm Optimization , Proceedings of the 1995 IEEE International Conference on Neural Networks, IEEE Service Center, Piscataway, NJ, 1995, 1942-1948

	Kennedy, J and Eberhart, R (1995) Particle Swarm Optimization , Proceedings of the 1995 IEEE International Conference on Neural Networks

	Khean, N, Kim, L, Martinez, J, Doherty, B, Fabbri, A, Gardner, N and Haeusler, M H (2018) The Introspection of Deep Neural Networks - Towards Illuminating the Black Box , Proceedings of CAADRIA 2018, Beijing, pp. 237-246

	Khean, N, Kim, L, Martinez, J, Doherty, B, Fabbri, A, Gardner, N and Haeusler, M. H. (2018) The Introspection of Deep Neural Networks - Towards Illuminating the Black Box , Proceedings of CAADRIA 2018, Beijing, pp. 237 - 246

	Khean, N, Lucas, K, Martinez, J, Doherty, B, Fabbri, A, Gardner, N and Haeusler, HH (2018) The Introspection of Deep Neural Networks: Towards Illuminating the Black Box , Conference: Learning, Adapting and Prototyping - 23rd International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), 2, pp. 237-246

	Khean, N., Kim, L., Martinez, J., Doherty, B., Fabbri, A., Gardner, N. and Haeusler, M.H. (2018) The Introspection of Deep Neural Networks - Towards Illuminating the Black Box - Training Architects Machine Learning via Grasshopper Definitions , Proceedings of the 23rd International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) 2018, Volume 2, Beijing, pp. 237-246

	Kim P (2017) Matlab Deep Learning , With Machine Learning, Neural Networks and Artificial Intelligence. NY: Apress, 2017, p. 130.

	Kim Y. (2014) Convolutional neural networks for sentence classification , arXiv preprintarXiv:1408.5882

	Kingma, D.P. et al. (2014) Semi-supervised learning with deep generative models , Advances in neural information processing systems, 27

	Koch, C. and S. Ullman (1985) Shifts in selective visual attention: towards the underlying neural circuitry , Human Neurobiology 4: p. 219-227

	Koch, G, Zemel, R and Salakhutdinov, R (2015) Siamese Neural Networks for One-shot Image Recognition , Proc. of 32nd Int. Conf. on Mach. Learning

	Koh I (2022) Architectural plasticity: the aesthetics of neural sampling , Architectural Design 2022; 92: 86–93

	Koh I (2022) Architectural plasticity: the aesthetics of neural sampling , AD 2022; 92: 86–93, DOI: 10.1002/ad.2818

	Koh, I. (2022) Architectural Plasticity: the Aesthetics of Neural Sampling , Architectural Design, 92(3), 86-93, Available at: https://doi.org/1.12/ad.2818.

	Kohonen T, Kaski, S, Lagus, K, Salojärvi, J, Honkela, J, Paatero, V and Saarela, A (2000) Self Organization of a Massive Document Collection , IEEE Transactions on Neural Networks, 11(3), pp. 574–585

	Kokinov, B. (1998) Analogy is like cognition: dynamic, emergent and context-sensitive , Advances in Analogy Research: Integration of Theory and Data from the Cognitive, Computational and Neural Sciences, NBU Press, Sofia, 96-105

	Konda, V. R. and Tsitsiklis, J. N. (2000) Actor-critic algorithms , Advances in neural information processing systems, pp. pp. 1008-1014

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