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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	R.D.Kalnins, L.Markosian, B.J.Meier, M.A,Kowalski, J.C.Lee, P.L.Davidson, M.Webb, J.F.Hughes, A.Finkelstein, WYSIWYG NPR (2002) Drawing strokes directly on 3d models , SIGGRAPH2002, pp.755-762

	Rabiner, L.R. and Juang B.H. (1986) An introduction to Hidden Markov Models , IEEE ASSP Mag., pp 4-16

	Raccoon, L.B.S. (1995) The Chaos Model and the Chaos Life Cycle , ACM Sigsoft Software Engineering Notes, January 1995.Vol.20, n.1, 56–57

	Raccoon, L.B.S. (1995) The Chaos Model and the Chaos Life Cycle , ACM SIGSOFT, Software Engineering Notes, 20 (1), 55- 66

	Radford A, Metz L and Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks, , http://arxiv.org/pdf/1511.06434v2 (2015).

	Radford A, Metz L and Chintala S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks, 2016 , ArXiv:1511.06434 [Cs] http://arxiv.org/abs/1511.06434

	Radford A. and Stevens, G. (1988) Role Play in Education: A Case Study from Architectural Computing , Journal of Architecture Education, 42(l): 18-23

	Radford, A, Metz, L and Chintala, S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks , arXiv preprint arXiv:1511.06434, 1, pp. 1-5

	Radford, A, Metz, L and Chintala, S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks. , Conference on Learning Representations (ICLR)

	Radford, A, Metz, L and Chintala, S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks , arXiv preprint, arXiv:1511.06434

	Radford, A., Metz, L. and Chintala, S. (2016) Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , ICLR

	Radford, A., Metz, L., & Chintala, S. (2016) Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , ArXiv.org. https://arxiv.org/abs/1511.06434v2

	Radford, L (2003) Gestures, speech, and the sprouting of signs: A semiotic-cultural approach to students' types of generalization , Mathematical thinking and learning, 5(1), pp. 37-70

	Radinsky, J., Milz, D., Zellner, M., Pudlock, K., Witek, C., Hoch, C., & Lyons, L. (2017) How planners and stakeholders learn with visualization tools: using learning sciences methods to examine planning processes , Journal of Environmental Planning and Management, 60(7), 1296–1323

	Radley JJ, Kabbaj M, Jacobson L, et al. (2011) Stress risk factors and stress-related pathology: neuroplasticity, epigenetics and endophenotypes , Stress 2011; 14: 481–497

	Rael, R., San Fratello, V., Curth, A. and Arja, L. (2020) Casa Covida , ACADIA Projects 2020, pp. 214-219

	Raes L, Michiels P, Adolphi T, et al (2021) DUET: a framework for building secure and trusted digital twins of smart cities , IEEE Internet Comput 2021; PP(99): 1

	Raggatt, H. (1993) Operations and Strategies for the Fringe , L. van Schaik (ed. ), Fin de siècle and the twenty-first century architectures of Melbourne, RMIT, Melbourne

	Rahbar, M., Mahdavinejad, M., Bemanian, M., Davaie Markazi, A. H., & Hovestadt, L. (2019) Generating Synthetic Space Allocation Probability Layouts Based on Trained Conditional-GANs , Applied Artificial Intelligence, 33(8), 689–705. https://doi.org/10.1080/08839514.2019.1592919

	Rahim, A (2009) Uniformity and variability in architectural practice , Spuybroek, L (eds), The architecture of variation, Thames and Hudson, London, pp. 40-47

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