Rede neural de processo Gaussiano (Portuguese Wikipedia)

Analysis of information sources in references of the Wikipedia article "Rede neural de processo Gaussiano" in Portuguese language version.

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11arxiv.org

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8harvard.edu

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2doi.org

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2openreview.net

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1worldcat.org

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1nips.cc

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arxiv.org

Guo, Chuan; Pleiss, Geoff; Sun, Yu; Weinberger, Kilian Q. (2017). «On calibration of modern neural networks». Proceedings of the 34th International Conference on Machine Learning-Volume 70. arXiv:1706.04599
Lee, Jaehoon; Bahri, Yasaman; Novak, Roman; Schoenholz, Samuel S.; Pennington, Jeffrey; Sohl-Dickstein, Jascha (2017). «Deep Neural Networks as Gaussian Processes». International Conference on Learning Representations. Bibcode:2017arXiv171100165L. arXiv:1711.00165
G. de G. Matthews, Alexander; Rowland, Mark; Hron, Jiri; Turner, Richard E.; Ghahramani, Zoubin (2017). «Gaussian Process Behaviour in Wide Deep Neural Networks». International Conference on Learning Representations. Bibcode:2018arXiv180411271M. arXiv:1804.11271
Novak, Roman; Xiao, Lechao; Lee, Jaehoon; Bahri, Yasaman; Yang, Greg; Abolafia, Dan; Pennington, Jeffrey; Sohl-Dickstein, Jascha (2018). «Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes». International Conference on Learning Representations. Bibcode:2018arXiv181005148N. arXiv:1810.05148
Garriga-Alonso, Adrià; Aitchison, Laurence; Rasmussen, Carl Edward (2018). «Deep Convolutional Networks as shallow Gaussian Processes». International Conference on Learning Representations. Bibcode:2018arXiv180805587G. arXiv:1808.05587
Borovykh, Anastasia. «A Gaussian Process perspective on Convolutional Neural Networks». arXiv:1810.10798
Tsuchida, Russell; Pearce, Tim. «Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks». arXiv:2002.08517
Yang, Greg (2019). «Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes» (PDF). Advances in Neural Information Processing Systems. Bibcode:2019arXiv191012478Y. arXiv:1910.12478
Novak, Roman; Bahri, Yasaman; Abolafia, Daniel A.; Pennington, Jeffrey; Sohl-Dickstein, Jascha (15 de fevereiro de 2018). «Sensitivity and Generalization in Neural Networks: an Empirical Study». International Conference on Learning Representations. Bibcode:2018arXiv180208760N. arXiv:1802.08760
Canziani, Alfredo; Paszke, Adam; Culurciello, Eugenio (4 de novembro de 2016). «An Analysis of Deep Neural Network Models for Practical Applications». Bibcode:2016arXiv160507678C. arXiv:1605.07678
Neyshabur, Behnam; Li, Zhiyuan; Bhojanapalli, Srinadh; LeCun, Yann; Srebro, Nathan (2019). «Towards understanding the role of over-parametrization in generalization of neural networks». International Conference on Learning Representations. Bibcode:2018arXiv180512076N. arXiv:1805.12076

doi.org

dx.doi.org

MacKay, David J. C. (1992). «A Practical Bayesian Framework for Backpropagation Networks». Neural Computation. 4: 448–472. ISSN 0899-7667. doi:10.1162/neco.1992.4.3.448
Neal, Radford M. (1996), «Priors for Infinite Networks», Bayesian Learning for Neural Networks, ISBN 978-0-387-94724-2, Lecture Notes in Statistics, 118, Springer New York, pp. 29–53, doi:10.1007/978-1-4612-0745-0_2

harvard.edu

ui.adsabs.harvard.edu

Lee, Jaehoon; Bahri, Yasaman; Novak, Roman; Schoenholz, Samuel S.; Pennington, Jeffrey; Sohl-Dickstein, Jascha (2017). «Deep Neural Networks as Gaussian Processes». International Conference on Learning Representations. Bibcode:2017arXiv171100165L. arXiv:1711.00165
G. de G. Matthews, Alexander; Rowland, Mark; Hron, Jiri; Turner, Richard E.; Ghahramani, Zoubin (2017). «Gaussian Process Behaviour in Wide Deep Neural Networks». International Conference on Learning Representations. Bibcode:2018arXiv180411271M. arXiv:1804.11271
Novak, Roman; Xiao, Lechao; Lee, Jaehoon; Bahri, Yasaman; Yang, Greg; Abolafia, Dan; Pennington, Jeffrey; Sohl-Dickstein, Jascha (2018). «Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes». International Conference on Learning Representations. Bibcode:2018arXiv181005148N. arXiv:1810.05148
Garriga-Alonso, Adrià; Aitchison, Laurence; Rasmussen, Carl Edward (2018). «Deep Convolutional Networks as shallow Gaussian Processes». International Conference on Learning Representations. Bibcode:2018arXiv180805587G. arXiv:1808.05587
Yang, Greg (2019). «Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes» (PDF). Advances in Neural Information Processing Systems. Bibcode:2019arXiv191012478Y. arXiv:1910.12478
Novak, Roman; Bahri, Yasaman; Abolafia, Daniel A.; Pennington, Jeffrey; Sohl-Dickstein, Jascha (15 de fevereiro de 2018). «Sensitivity and Generalization in Neural Networks: an Empirical Study». International Conference on Learning Representations. Bibcode:2018arXiv180208760N. arXiv:1802.08760
Canziani, Alfredo; Paszke, Adam; Culurciello, Eugenio (4 de novembro de 2016). «An Analysis of Deep Neural Network Models for Practical Applications». Bibcode:2016arXiv160507678C. arXiv:1605.07678
Neyshabur, Behnam; Li, Zhiyuan; Bhojanapalli, Srinadh; LeCun, Yann; Srebro, Nathan (2019). «Towards understanding the role of over-parametrization in generalization of neural networks». International Conference on Learning Representations. Bibcode:2018arXiv180512076N. arXiv:1805.12076

nips.cc

papers.nips.cc

Yang, Greg (2019). «Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes» (PDF). Advances in Neural Information Processing Systems. Bibcode:2019arXiv191012478Y. arXiv:1910.12478

openreview.net

Novak, Roman; Bahri, Yasaman; Abolafia, Daniel A.; Pennington, Jeffrey; Sohl-Dickstein, Jascha (15 de fevereiro de 2018). «Sensitivity and Generalization in Neural Networks: an Empirical Study». International Conference on Learning Representations. Bibcode:2018arXiv180208760N. arXiv:1802.08760
Canziani, Alfredo; Paszke, Adam; Culurciello, Eugenio (4 de novembro de 2016). «An Analysis of Deep Neural Network Models for Practical Applications». Bibcode:2016arXiv160507678C. arXiv:1605.07678

worldcat.org

MacKay, David J. C. (1992). «A Practical Bayesian Framework for Backpropagation Networks». Neural Computation. 4: 448–472. ISSN 0899-7667. doi:10.1162/neco.1992.4.3.448