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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	Yoshimura, Yuji, Cai, Bill, Wang, Zhoutong and Ratti, Carlo (2019) Deep learning architect: Classification for architectural design through the eye of artificial intelligence , Lecture Notes in Geoinformation and Cartography, NA, pp. 249-265

	Young, T., Hazarika, D., Poria, S., & Wang, Z. (2018) Recent trends in deep learning based natural language processing [review article] , Ieee Computational Intelligence Magazine, 13(3), 55-75. https://doi.org/10.1109/mci.2018.2840738

	Yousif, S and Bolojan, D (2021) Deep-Performance - Incorporating Deep Learning for Automating Building Performance Simulation in Generative Systems , Proceedings of CAADRIA 2021, pp. 151-160

	Yousif, S and Yan, W (2018) Clustering Forms for Enhancing Architectural Design Optimization , 23rd CAADRIA Conference: Learning, Adapting and Prototyping, Beijing, pp. 431-440

	Yousif, S. & Bolojan, D. (2021) Deep-Performance: Incorporating Deep Learning for Automating Building Performance Simulation in Generative Systems , Projections, the 26th Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong, China. http://papers.cumincad.org/cgi-bin/works/paper/caadria2021_052

	Yousif, S., & Bolojan, D. (2021) Deep-Performance - Incorporating Deep Learning for Automating Building Performance Simulation in Generative Systems , Proc. Of 26th CAADRIA

	Yousif, S., & Bolojan, D. (2021) Deep-Performance: Incorporating Deep Learning for Automating Building Performance Simulation in Generative Systems , 26th International Conference on Computer-Aided Architectural Design ResearchAsia: Projections, CAADRIA 2020 (pp. 151-160). The Association for Computer-Aided Architectural Design ResearchAsia (CAADRIA)

	Yousif, S., & Bolojan, D. (2021) Deep-performance: Incorporating deep learning for automating building performance simulation in generative systems , Projections - Proceedings of the 26th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2021, 1

	YOUSIF, S., & BOLOJAN, D. (2022) Deep learning-based surrogate modeling for performance-driven generative design systems , Proc of the 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), CAADRIA (pp. 363-372)

	Yousif, S., & Bolojan, D. (2022) Deep Learning-Based Surrogate Modeling for Performance-Driven Generative Design Systems , Proc of the 27th CAADRIA (pp. 363-372)

	Yousif, Shermeen, and Wei Yan. (2018) Clustering Forms for Enhancing Architectural Design Optimization , Learning, Adapting and Prototyping; The 23rd Conference of the Association for Computer-Aided Architectural Design Research Asia (CAADRIA). Beijing, China

	Yousof, G. S. (2001) Research into Wayang Kulit and teaching of the genre in institutions of higher learning in Malaysia , A. Rahim and A. Ghazali (eds. ), Proceedings of the Festival Wayang Nusantara: Wayang Dalam Bayang, pp. 96–120

	Yu, C., J. Liu, and Shamim Nemati (2020) Reinforcement Learning in Healthcare: A Survey , arXiv preprint. arXiv:1908.08796

	Yu, DK, Haeusler, MH, Simon, K and Fabbri, A (2018) BiCycle Pathway Generation Through a Weighted Digital Slime Mold Algorithm via Topographical Analysis , Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, pp. 381-390

	Yu, P., Wang, S. (2024) An Examination and Analysis of the Integration of Artificial Intelligence and Gamification in the Pedagogy of Chinese Higher Education. In Engaged Learning and Innovative Teaching in Higher Education , Digital Technology, Professional Competence, and Teaching Pedagogies, pp. 29-46, Singapore: Springer Nature Singapore

	Yu, T, Quillen, D, He, Z, Julian, R, Hausman, K, Finn, C and Levine, S (2020) Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning , Conference on Robot Learning, pp. 1094-1100

	Yu, T. et al. (2021) Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning , arXiv:1910.10897 [cs, stat] [Preprint]. Available at: http://arxiv.org/abs/1910.10897 (Accessed: 31 January 2022)

	Yu, Y., Hur, T., Jung, J. & Jang, I. G. (2019) Deep learning for determining a near-optimal topological design without any iteration , Structural Multidisciplinary Optimization, 59(3), 787-799. doi:https://doi.org/10.1007/s00158-018-2101-5

	Yuan, L., Lu, W., Xue, F., & Li, M. (2023) Building feature-based machine learning regression to quantify urban material stocks: A Hong Kong study , Journal of Industrial Ecology, 27(1), 336-349. https://doi-org.libproxy1.nus.edu.sg/10.1111/jiec.13348

	Yuan, P. F., Chai, H., & Jin, J. (2018) Digital form-finding and fabrication of strained gridshells with complex geometries , Weiguo Xu (Ed.),23rd International Conference Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting (Vol. 1, pp. 267–276). CAADRIA

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