Problema della scomparsa del gradiente (Italian Wikipedia)

Analysis of information sources in references of the Wikipedia article "Problema della scomparsa del gradiente" in Italian language version.

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6doi.org

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archive.org

Razvan Pascanu, Tomas Mikolov e Yoshua Bengio, On the difficulty of training Recurrent Neural Networks, 16 febbraio 2013.
Jürgen Schmidhuber, Deep learning in neural networks: An overview, in Neural Networks, vol. 61, 2015, pp. 85-117, DOI:10.1016/j.neunet.2014.09.003, PMID 25462637, arXiv:1404.7828.
Andreas Veit, Michael Wilber e Serge Belongie, Residual Networks Behave Like Ensembles of Relatively Shallow Networks, 20 maggio 2016.

arxiv.org

(EN) Garrett B. Goh, Nathan O. Hodas e Abhinav Vishnu, Deep learning for computational chemistry, in Journal of Computational Chemistry, vol. 38, n. 16, 15 giugno 2017, pp. 1291-1307, DOI:10.1002/jcc.24764/abstract, PMID 28272810, arXiv:1701.04503.
Jürgen Schmidhuber, Deep learning in neural networks: An overview, in Neural Networks, vol. 61, 2015, pp. 85-117, DOI:10.1016/j.neunet.2014.09.003, PMID 25462637, arXiv:1404.7828.
Kaiming He, Xiangyu Zhang, Shaoqing Ren e Jian Sun, Deep Residual Learning for Image Recognition, in arXiv:1512.03385 [cs], 10 dicembre 2015.

colah.github.io

Cristopher Olah, Understanding LSTM networks, su colah.github.io.

doi.org

dx.doi.org

(EN) Garrett B. Goh, Nathan O. Hodas e Abhinav Vishnu, Deep learning for computational chemistry, in Journal of Computational Chemistry, vol. 38, n. 16, 15 giugno 2017, pp. 1291-1307, DOI:10.1002/jcc.24764/abstract, PMID 28272810, arXiv:1701.04503.
Jürgen Schmidhuber, Deep learning in neural networks: An overview, in Neural Networks, vol. 61, 2015, pp. 85-117, DOI:10.1016/j.neunet.2014.09.003, PMID 25462637, arXiv:1404.7828.
G. E. Hinton, S. Osindero e Y. Teh, A fast learning algorithm for deep belief nets (PDF), in Neural Computation, vol. 18, n. 7, 2006, pp. 1527-1554, DOI:10.1162/neco.2006.18.7.1527, PMID 16764513.
G. Hinton, Deep belief networks, in Scholarpedia, vol. 4, n. 5, 2009, p. 5947, Bibcode:2009SchpJ...4.5947H, DOI:10.4249/scholarpedia.5947.
Sepp Hochreiter e Jürgen Schmidhuber, Long Short-Term Memory, in Neural Computation, vol. 9, n. 8, 1997, pp. 1735-1780, DOI:10.1162/neco.1997.9.8.1735, PMID 9377276.
A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke e J. Schmidhuber, A Novel Connectionist System for Improved Unconstrained Handwriting Recognition, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, n. 5, 2009, pp. 855-868, DOI:10.1109/tpami.2008.137.

harvard.edu

adsabs.harvard.edu

G. Hinton, Deep belief networks, in Scholarpedia, vol. 4, n. 5, 2009, p. 5947, Bibcode:2009SchpJ...4.5947H, DOI:10.4249/scholarpedia.5947.

idsia.ch

people.idsia.ch

Sepp Hochreiter's Fundamental Deep Learning Problem (1991), su people.idsia.ch. URL consultato il 7 gennaio 2017.

mlr.press

proceedings.mlr.press

(EN) Xavier Glorot, Antoine Bordes e Yoshua Bengio, Deep Sparse Rectifier Neural Networks, in PMLR, 14 giugno 2011.

nih.gov

ncbi.nlm.nih.gov

(EN) Garrett B. Goh, Nathan O. Hodas e Abhinav Vishnu, Deep learning for computational chemistry, in Journal of Computational Chemistry, vol. 38, n. 16, 15 giugno 2017, pp. 1291-1307, DOI:10.1002/jcc.24764/abstract, PMID 28272810, arXiv:1701.04503.
Jürgen Schmidhuber, Deep learning in neural networks: An overview, in Neural Networks, vol. 61, 2015, pp. 85-117, DOI:10.1016/j.neunet.2014.09.003, PMID 25462637, arXiv:1404.7828.
G. E. Hinton, S. Osindero e Y. Teh, A fast learning algorithm for deep belief nets (PDF), in Neural Computation, vol. 18, n. 7, 2006, pp. 1527-1554, DOI:10.1162/neco.2006.18.7.1527, PMID 16764513.
Sepp Hochreiter e Jürgen Schmidhuber, Long Short-Term Memory, in Neural Computation, vol. 9, n. 8, 1997, pp. 1735-1780, DOI:10.1162/neco.1997.9.8.1735, PMID 9377276.

oadoi.org

(EN) Garrett B. Goh, Nathan O. Hodas e Abhinav Vishnu, Deep learning for computational chemistry, in Journal of Computational Chemistry, vol. 38, n. 16, 15 giugno 2017, pp. 1291-1307, DOI:10.1002/jcc.24764/abstract, PMID 28272810, arXiv:1701.04503.
Sepp Hochreiter e Jürgen Schmidhuber, Long Short-Term Memory, in Neural Computation, vol. 9, n. 8, 1997, pp. 1735-1780, DOI:10.1162/neco.1997.9.8.1735, PMID 9377276.
A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke e J. Schmidhuber, A Novel Connectionist System for Improved Unconstrained Handwriting Recognition, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, n. 5, 2009, pp. 855-868, DOI:10.1109/tpami.2008.137.

scholarpedia.org

G. Hinton, Deep belief networks, in Scholarpedia, vol. 4, n. 5, 2009, p. 5947, Bibcode:2009SchpJ...4.5947H, DOI:10.4249/scholarpedia.5947.

toronto.edu

cs.toronto.edu

G. E. Hinton, S. Osindero e Y. Teh, A fast learning algorithm for deep belief nets (PDF), in Neural Computation, vol. 18, n. 7, 2006, pp. 1527-1554, DOI:10.1162/neco.2006.18.7.1527, PMID 16764513.

uni-bonn.de

ais.uni-bonn.de

Sven Behnke, Hierarchical Neural Networks for Image Interpretation. (PDF), Lecture Notes in Computer Science, vol. 2766, Springer, 2003.

web.archive.org

Andrew Trask, Grokking Deep Learning, Manning, 2019, ISBN 978-1617293702. URL consultato l'8 ottobre 2021 (archiviato dall'url originale il 14 settembre 2021).