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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	I. Durugkar, I. Gemp, and S. Mahadevan (2016) Generative Multi-Adversarial Networks , ArXiv Preprint

	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)

	Goodfellow, Ian (2017) NIPS 2016 Tutorial: Generative Adversarial Networks , ArXiv:1701.00160 [Cs], April. http://arxiv.org/abs/1701.00160

	Pfau, D and Vinyals, O (2016) Connecting Generative Adversarial Networks and Actor-Critic Methods , arXiv:1610.01945 [cs, stat]

	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, 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

	Choi, EM (2017) Generating Multi-label Discrete Patient Records using Generative Adversarial Networks , Proceedings of Machine Learning for Healthcare, pp. 1-20

	I. Albuquerque, J. Monteiro, T. Doan, B. Considine, T. Falk, and I. Mitliagkas (2019) Multi-Objective Training of Generative Adversarial Networks with Multiple Discriminators , Proceedings of the 36th International Conference on Machine Learning. PMLR 97: 202–211

	A. Brock, T. Lim, J. M. Ritchie, and N. Weston (2016) Generative and Discriminative Voxel Modeling with Convolutional Neural Networks , ArXiv Preprint

	Brock, A, Lim, T and Ritchie, JM (2016) Generative and discriminative voxel modeling with convolutional neural networks , arXiv preprint arXiv:1608.04236

	Chen X., Duan Y., Houthooft R. et al. (2016) InfoGAN: interpretable representation learning by information maximizing generative adversarial nets , NIPS, https://arxiv.org/abs/1606.03657

	Chen, Lujie, and Lawrence Sass (2016) Generative Computer-Aided Design: Multi-Modality Large-Scale Direct Physical Production , Computer-Aided Design and Applications 14 (1): 83–94. doi:10.1080/16864360.2016.1199758

	Chen, Lujie, and Lawrence Sass (2016) Generative Computer-Aided Design: Multi-Modality Large-Scale Direct Physical Production , Computer-Aided Design and Applications 14 (1): 83–94. doi:10.1080/16864360.2016.1199758

	Isola, P., Zhu, J., Zhou, T. & Efros, A.A. (2016) Image-to-Image Translation with Conditional Adversarial Networks , 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5967-5976

	Isola, Phillip, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros (2016) Image-to-Image Translation with Conditional Adversarial Networks , CoRR. abs/1611.07004

	J. Wu, C. Zhang, T. Xue, W. T. Freeman, and J. B. Tenenbaum (2016) Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling , Proceedings of the 30th International Conference on Neural Information Processing Systems. 82–90

	Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, And Lee, H. (2016) Generative adversarial text-to-image synthesis , ICML, 33 rd International Conference on Machine Learning,New York, NY, USA

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