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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	Nielsen, N. M. (2015) Neural Networks and Deep Learning , Detremination Press

	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 (2015) Unsupervised representation learning with deep convolutional generative adversarial networks , arXiv preprint arXiv:1511.06434, 1, pp. 1-5

	Radford, Alec, Luke Metz, and Soumith Chintala (2015) Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , ICLR (International Conference on Learning Representations). arXiv:1511.06434

	Radford, Alec, Luke Metz, and Soumith Chintala (2015) Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , arXiv preprint. arXiv:1511.06434

	Sarvepalli, Sarat Kumar (2015) Deep Learning in Neural Networks: The Science behind an Artificial Brain , doi.org/10.13140/RG.2.2.22512.71682

	Schmidhuber, J (2015) Deep learning in neural networks: An overview , Neural networks, 61, pp. 85-117

	Schmidhuber, J (2015) Deep learning in neural networks: An overview , Neural networks, 61, pp. 85-117

	Schmidhuber, J (2015) Deep Learning in Neural Networks: An Overview , Neural Networks, 61, pp. 85-117

	Schmidhuber, J (2015) Deep learning in neural networks: An overview , Neural networks, 61, pp. 85-117

	Schmidhuber, J. (2015) Deep learning in neural networks: An overview , Neural networks, 61, pp. 85-117

	Simonyan, K and Andrew, Z (2015) Very Deep Convolutional Networks for Large-Scale Image Recognition , International Conference on Learning Representations 2015

	Simonyan, K and Zisserman, A (2015) Very deep convolutional networks for large-scale image recognition , International Conference on Learning Representations

	Sohl-Dickstein J, Weiss E, Maheswaranathan N, et al (2015) Deep Unsupervised Learning using Nonequilibrium Thermodynamics , Bach F and Blei D (eds) Proceedings of the 32nd International Conference on Machine Learning

	Sohl-Dickstein J, Weiss EA, Maheswaranathan N, et al (2015) Deep unsupervised learning using nonequilibrium thermodynamics , Epub ahead of print November 2015 DOI: 10.48550/arXiv.1503.03585

	Sohn K, Lee H and Yan X (2015) Learning structured output representation using deep conditional generative models , Advances in Neural Information Processing Systems. Curran Associates, Inc

	Sohn, K, Lee, H and Yan, X (2015) Learning structured output representation using deep conditional generative models , NIPS 2015

	Timothy P, Lillicrap, Jonathan J, Hunt, Alexander, Pritzel and Nicolas, Heess (2015) Continuous control with deep reinforcement learning , arXiv preprint arXiv:1509.02971, ., p. .

	Yabuki, K, et al. (2015) Basic study on the method of landscape evaluation with deep learning , Proceeding of the annual symposium of Japan Society of Graphic Science, pp. 23-28

	(2015) Effect of Engagement and Collaborative Learning on Satisfaction through the use of Social Media on Malaysian Higher Education , Research of Applied Sciences, Engineering and Technology

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