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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	Shen, J. I. A. Q. I., Liu, C. H. U. A. N., Ren, Y., & Zheng, H. (2020) Machine Learning Assisted Urban Filling , 25th CAADRIA Conference Volume 2, Chulalongkorn University, Bangkok, Thailand, (pp. 679-688), Available at: https://doi.org/1.52842/conf.caadria.22.2.679.

	Shen, Q., Zeng, W., Ye, Y., Arisona, S. M., Schubiger, S., Burkhard, R. & Qu, H. (2018) StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views , IEEE transactions on visualization and computer graphics, 24(1), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159Verma, D., Jana, A., & Ramamritham, K. (2018). Quantifying Urban Surroundings Using Deep Learning Techniques: A New Proposal. Urban Science, 2(3), 78. MDPI AG. Retrieved from http://dx.doi.org/10.3390/urbansci2030078

	Sun, B., Zhang, Q., & Cao, S. (2020) Development and implementation of a self-optimizable smart lighting system based on learning context in classroom , International Journal of Environmental Research and Public Health, 17(4). https://doi.org/10.3390/ijerph17041217

	Sun, L., Tang, L., Shao, G., Qiu, Q., Lan, T., & Shao, J. (2019) A Machine Learning-Based Classification System for Urban Built-Up Areas Using Multiple Classifiers and Data Sources , Remote Sensing, 1(12). Available at: https://doi.org/10.3390/rs1201009

	Sutskever, I., Vinyals, O., and Le, Q. V. (2014) Sequence to Sequence Learning with Neural Networks , Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems (Vol. 27). Curran Associates, Inc

	Tan, M., & Le, Q. (2019) Efficientnet: Rethinking Model Scaling for Convolutional Neural Networks , International conference on machine learning (pp. 615-6114). PMLR, Available at: https://doi.org/1.4855/arXiv.195.11946.

	van Hassel, H, Guez, A and Silver, D (2016) Deep Reinforcement Learning with Double Q-learning , Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, pp. 2094-2100

	Wang Q, L’Yi S, Gehlenborg N (2023) DRAVA: Aligning Human Concepts with Machine Learning Latent Dimensions for the Visual Exploration of Small Multiples , CHI’23, April 23–28, 2023

	Wang Y, Yao Q, Kwok JT, et al (2020) Generalizing from a few examples: a survey on few-shot learning , ACM Comput Surv2020; 53: 1–34

	Wang, M, Yu, L, Zheng, D, Gan, Q, Gai, Y and Ye, Z (2019) Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs , arXiv:1909.01315

	Xu, Q. M., Garcia Del Castillo Lopez, J. L., & Samuelson, H. W. (2023) Towards a Decision Framework Integrating Physics-Based Simulation and Machine Learning in Conceptual Design , HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference (pp. 371-380). The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA). https://doi.org/10.52842/conf.caadria.2023.2.371

	Yan, X. & Crookes, R. J. (2009) Reduction potentials of energy demand and GHG emissions in China's road transport sector , Energy Policy, 37(2), 658-668. https://doi.org/10.1016/j.enpol.2008.10.008Yang, B., Wang, J., Clark, R., Hu, Q., Wang, S., Markham, A., & Trigoni, N. (20). Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. In 33rd International Conference on Neural Information Processing Systems (pp. 2940-2949)

	Yang, Q., Scuito, A., Zimmerman, J., Forlizzi, J., & Steinfeld, A. (2018) Investigating How Experienced UX Designers Effectively Work with Machine Learning , Proceedings of the 2018 Designing Interactive Systems Conference, 585-596. https://doi.org/10.1145/3196709.3196730

	Ye, Y., Zeng, W., Shen, Q., Zhang, X., & Lu, Y. (2019) The Visual Quality of Streets: a Human-centred Continuous Measurement Based on Machine Learning Algorithms and Street View Images , Environment and Planning B: Urban Analytics and City Science, 46(8), 1439-1457. http://dx.doi.org/1.1177/239988319828734

	Ye, Y., Zeng, W., Shen, Q., Zhang, X., & Lu, Y. (2019) The Visual Quality of Streets: a Human-centred Continuous Measurement Based on Machine Learning Algorithms and Street View Images , Environment and Planning B: Urban Analytics and City Science, 46(8), 1439-1457. Available at: https://doi.org/1.1177/239988319828734

	Zhang, Y., Zhang, Q., Zhao, Y., Deng, Y., & Zheng, H. (2022) Urban Spatial Risk Prediction and Optimization Analysis of Poi Based on Deep Learning from the Perspective of an Epidemic , International Journal of Applied Earth Observation and Geoinformation, 112, 12942. Available at: https://doi.org/1.116/j.jag.222.12942

	Zhao, Z.-Q., Xie, B.-J., Cheung, Y., Wu, X., (2015) Plant Leaf Identification via a Growing Convolution Neural Network with Progressive Sample Learning , Cremers, D., Reid, I., Saito, H., Yang, M.-H. (Eds.), Computer Vision -- ACCV 2014, Lecture Notes in Computer Science. Springer International Publishing, Cham, pp. 348-361. https://doi.org/10.1007/978-3-319-16808-1_24

	Zhao, Z.-Q., Zheng, P., Xu, S.-T., Wu, X., (2019) Object Detection With Deep Learning: A Review , IEEE Trans. Neural Netw. Learn. Syst. 30, 3212-3232. https://doi.org/10.1109/TNNLS.2018.2876865

	Zheng, T., Pan, Q., Zhang, X., Wang, C., Yan, Y., & Van De Voorde, T. (2024) Research Note: Linking sensory perceptions with landscape elements through a combined approach based on prior knowledge and machine learning , Landscape and Urban Planning, 242, 104928. https://doi.org/10.1016/j.landurbplan.2023.104928

	Zuo, Q., & MaloneBeach, E. E. (2010) A comparison of learning experience, workload, and outcomes in interior design education using a hand or hybrid approach , Family and Consumer Sciences Research Journal, 39(1), 90-106

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