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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	Ahmed, M., & OLA, F. (2022) Graph attention networks , Journal of Heritage and Design, 2(7), 315-342

	Han, B., Zhang, X., & Ren, S. (2022) PU-gacnet: Graph attention convolution network for point cloud Upsampling , Image and Vision Computing, 118, 104371. https://doi.org/10.1016/j.imavis.2021.104371

	Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2022) Semi-supervised classification with graph convolutional networks , Communications of the ACM, 65(1), 99-106. https://doi.org/10.1145/3503250

	Zhang, H. et al. (2019) Self-Attention Generative Adversarial Networks , arXiv:1805.08318 [cs, stat] [Preprint]. Available at: http://arxiv.org/abs/1805.08318 (Accessed: 30 March 2022)

	Velièkoviæ, P. et al. (2018) Graph attention networks , arXiv (Cornell University) https://doi.org/10.17863/cam.48429

	Alani, M. and Al-Kaseem, B. (2021) Fill in the Blanks: Deep Convolutional Generative Adversarial Networks to Investigate the Virtual Design Space of Historical Islamic Patterns in Proceedings of the 9th ASCAAD Conference , Cairo, Egypt [Virtual Conference], pp. 614-621. Available at: http://papers.cumincad.org/data/works/att/ascaad2021_093.pdf (Accessed 6 March 2022)

	Allen-Dumas, M.R. et al. (2022) Generative adversarial networks for ensemble projections of future urban morphology , Proceedings of the 5th ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities. SIGSPATIAL '22: The 30th International Conference on Advances in Geographic Information Systems, Seattle Washington: ACM, pp. 1-6

	Alymani A, Jabi W and Corcoran P (2022) Graph machine learning classification using architectural 3D topological models , Simulation 2022; 003754972211058. DOI: 10.1177/00375497221105894.

	Alymani, A., Jabi, W. and Corcoran, P. (2022) Graph machine learning classification using architectural 3D topological models , Simulation: Transactions ofthe Society for Modeling and Simulation International. doi: 10.1177/00375497221105894

	Alymani, A., Jabi, W., & Corcoran, P. (2022) Graph machine learning classification using architectural 3D topological models , SIMULATION: Transactions of The Society for Modeling and Simulation International, Online, 1-15. https://doi.org/10.1177/00375497221105894

	Andris, C., & Sarkar, D. (2022) Social networks in space , Chapters, 400-415

	Bank, M., Sandor, V., Kraft, R., Antholzer, S., Berger, M., Fabini, T., Kovacs, B., Hell, T., Rutzinger, S. and Schinegger, K., (2022) Hybrid immediacy: designing with artificial neural networks through physical concept modelling , Design Modelling Symposium Berlin (pp. 13-23). Cham: Springer International Publishing

	Bisht, S., Shekhawat, K., Upasani, N., Jain, R., Tiwaskar, R., & Hebbar, C. (2022) Transforming an Adjacency Graph Into Dimensioned Floorplan , Computer Graphics Forum, 41(6), 5-22. Available at: https://doi.org/1.1111/cgf.14451

	Boeing, G. (2017) OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks , Computers, Environment and Urban Systems, 65, 126–139. https://doi.org/10.1016/j.compenvurbsys.2017.05.004Chestnut, J. (2018, March 28). Pedestrians first: Tools for a Walkable City. Institute for Transportation and Development Policy. Retrieved February 3, 2022, from https://www.itdp.org/publication/walkability-tool/

	del Campo, M. (2021) Architecture, Language and AI - Language,Attentional Generative Adversarial Networks (AttnGAN) and Architecture Design , A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 211-220. CUMINCAD. Available at: http://papers.cumincad.org/cgi-bin/works/2015%20+dave=2:/Show?caadria2021_389 (Accessed: 5 January 2022)

	Dong, Z. (2022) Perceiving Fabric Immersed in Time, an Exploration of Urban Cognitive Capabilities of Neural Networks , Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 263-272. CUMINCAD

	Goodfellow, I. (2017) NIPS 2016 Tutorial: Generative Adversarial Networks , arXiv:1701.00160 [cs] [Preprint]. Available at: http://arxiv.org/abs/1701.00160 (Accessed: 1 April 2022)

	Halici, S. M., & Gül, L. F. (2022) Utilizing Generative Adversarial Networks for Augmenting Architectural Massing Studies: AI-assisted Mixed Reality , B. Pak, G. Wurzer, & R. Stouffs (Eds.), Co-creating the Future: Inclusion in and through Design, eCAADe 2022, Volume 1, (pp. 323-330). Ghent

	Hsieh, M. C., Zhang, Y. Q., Dong, R., Lee, Y. C., & Ni, C. Y. (2022) Use of EEG signals, cortisol secretion, and task performance to evaluate the effects of different lighting environments on concentration level in a sustained attention task , International Journal of Industrial Ergonomics, 92, Article 103371. https://doi.org/10.1016/j.ergon.2022.103371

	Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022) A Survey on Knowledge Graphs: Representation, Acquisition, and Applications , IEEE Transactions on Neural Networks and Learning Systems, 33(2), 494-514. https://doi.org/10.1109/TNNLS.2021.3070843

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