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Y. Kiarashinejad, S. Abdollahrameazni, and A. Adibi, “Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures,” arXiv preprint arXiv:1902.03865 (2019).

Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar, and A. Adibi, “Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices,” Adv. Theory Simulations0, 1900088.



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I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, and H. Suchowski, “Plasmonic nanostructure design and characterization via Deep Learning,” Light. Sci. Appl. 760 (2018).

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W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector,” in European Conference on Computer Vision, (2016), pp. 21–37.



Bhaskaran, M.

D. Headland, E. Carrasco, S. Nirantar, W. Withayachumnankul, P. Gutruf, J. Schwarz, D. Abbott, M. Bhaskaran, S. Sriram, J. Perruisseau-Carrier, and C. Fumeaux, “Dielectric resonator reflectarray as high-efficiency nonuniform terahertz metasurface,” ACS Photonics 3, 1019–1026 (2016).

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J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 44206 (2018).

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D. Headland, E. Carrasco, S. Nirantar, W. Withayachumnankul, P. Gutruf, J. Schwarz, D. Abbott, M. Bhaskaran, S. Sriram, J. Perruisseau-Carrier, and C. Fumeaux, “Dielectric resonator reflectarray as high-efficiency nonuniform terahertz metasurface,” ACS Photonics 3, 1019–1026 (2016).

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J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 (2018).



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C. Chen and S. Li, “Valence electron density-dependent pseudopermittivity for nonlocal effects in optical properties of metallic nanoparticles,” ACS Photonics 5, 2295–2304 (2018).

[Crossref]

Chen, H. T.

C. C. Chang, L. Huang, J. Nogan, and H. T. Chen, “Invited Article: Narrowband terahertz bandpass filters employing stacked bilayer metasurface antireflection structures,” APL Photonics 3051602 (2018).

[Crossref]

Chen, K.

T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed Representations of Words and Phrases and their Compositionality,” in Advances in Neural Information Processing Systems, (NIPS, 2013), pp. 1–9.



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W. Ma, F. Cheng, and Y. Liu, “Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials,” ACS Nano 12, 6326–6334 (2018).

[Crossref] [PubMed]

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J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson, “Understanding neural networks through deep visualization,” arXiv preprint arXiv:1506.06579 (2015).



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T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed Representations of Words and Phrases and their Compositionality,” in Advances in Neural Information Processing Systems, (NIPS, 2013), pp. 1–9.



Cybenko, G.

G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Math. Contr. Sign. Syst. 2, 303–314 (1989).

[Crossref]

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T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed Representations of Words and Phrases and their Compositionality,” in Advances in Neural Information Processing Systems, (NIPS, 2013), pp. 1–9.



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J. Sautter, I. Staude, M. Decker, E. Rusak, D. N. Neshev, I. Brener, and Y. S. Kivshar, “Active tuning of all-dielectric metasurfaces,” ACS Nano 9, 4308–4315 (2015).

[Crossref] [PubMed]

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J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 44206 (2018).

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J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 (2018).



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S. Sun, Z. Zhou, C. Zhang, Y. Gao, Z. Duan, S. Xiao, and Q. Song, “All-dielectric full-color printing with tio2 metasurfaces,” ACS Nano 11, 4445–4452 (2017). PMID: .

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V. Dumoulin and F. Visin, “A guide to convolution arithmetic for deep learning,” arXiv preprint arXiv:1603.07285 (2016).



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M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).

[Crossref] [PubMed]

Erhan, D.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector,” in European Conference on Computer Vision, (2016), pp. 21–37.



Fan, K.

Fergus, R.

M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus, “Deconvolutional networks,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (IEEE, 2010), pp. 2528–2535.



Fu, C. Y.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector,” in European Conference on Computer Vision, (2016), pp. 21–37.



Fuchs, T.

J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson, “Understanding neural networks through deep visualization,” arXiv preprint arXiv:1506.06579 (2015).



Fumeaux, C.

D. Headland, E. Carrasco, S. Nirantar, W. Withayachumnankul, P. Gutruf, J. Schwarz, D. Abbott, M. Bhaskaran, S. Sriram, J. Perruisseau-Carrier, and C. Fumeaux, “Dielectric resonator reflectarray as high-efficiency nonuniform terahertz metasurface,” ACS Photonics 3, 1019–1026 (2016).

[Crossref]

Gao, Y.

S. Sun, Z. Zhou, C. Zhang, Y. Gao, Z. Duan, S. Xiao, and Q. Song, “All-dielectric full-color printing with tio2 metasurfaces,” ACS Nano 11, 4445–4452 (2017). PMID: .

[Crossref] [PubMed]

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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Advances in Neural Information Processing Systems, (NIPS, 2017), Nips.



Gutruf, P.

D. Headland, E. Carrasco, S. Nirantar, W. Withayachumnankul, P. Gutruf, J. Schwarz, D. Abbott, M. Bhaskaran, S. Sriram, J. Perruisseau-Carrier, and C. Fumeaux, “Dielectric resonator reflectarray as high-efficiency nonuniform terahertz metasurface,” ACS Photonics 3, 1019–1026 (2016).

[Crossref]

Headland, D.

D. Headland, E. Carrasco, S. Nirantar, W. Withayachumnankul, P. Gutruf, J. Schwarz, D. Abbott, M. Bhaskaran, S. Sriram, J. Perruisseau-Carrier, and C. Fumeaux, “Dielectric resonator reflectarray as high-efficiency nonuniform terahertz metasurface,” ACS Photonics 3, 1019–1026 (2016).

[Crossref]

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Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar, and A. Adibi, “Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices,” Adv. Theory Simulations0, 1900088.



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Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436 (2015).

[Crossref] [PubMed]

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A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems, (2012), pp. 1097–1105.



Hornik, K.

K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks 2, 359–366 (1989).

[Crossref]

Huang, L.

C. C. Chang, L. Huang, J. Nogan, and H. T. Chen, “Invited Article: Narrowband terahertz bandpass filters employing stacked bilayer metasurface antireflection structures,” APL Photonics 3051602 (2018).

[Crossref]

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M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).

[Crossref] [PubMed]

Jing, L.

Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljačić, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6, 1168–1174 (2019).

[Crossref] J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 44206 (2018).

[Crossref] [PubMed]

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J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 44206 (2018).

[Crossref] [PubMed]

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X. Liu, T. Tyler, T. Starr, A. F. Starr, N. M. Jokerst, and W. J. Padilla, “Taming the blackbody with infrared metamaterials as selective thermal emitters,” Phys. Rev. Lett. 107, 045901 (2011).

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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Advances in Neural Information Processing Systems, (NIPS, 2017), Nips.



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H. Kabir, Y. Wang, M. Yu, and Q. Zhang, “Neural Network Modeling and Applications to Microwave Design,” IEEE Trans. Microw. Theory Tech. 56, 867 (2008).

[Crossref]

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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Advances in Neural Information Processing Systems, (NIPS, 2017), Nips.



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M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).

[Crossref] [PubMed]

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D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures,” ACS Photonics 5, 1365–1369 (2018).

[Crossref]

Kiarashinejad, Y.

Y. Kiarashinejad, S. Abdollahrameazni, and A. Adibi, “Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures,” arXiv preprint arXiv:1902.03865 (2019).

Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar, and A. Adibi, “Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices,” Adv. Theory Simulations0, 1900088.



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I. Sajedian, J. Kim, and J. Rho, “Predicting resonant properties of plasmonic structures by deep learning,” arXiv preprint arXiv:1805.00312 (2018).



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J. Sautter, I. Staude, M. Decker, E. Rusak, D. N. Neshev, I. Brener, and Y. S. Kivshar, “Active tuning of all-dielectric metasurfaces,” ACS Nano 9, 4308–4315 (2015).

[Crossref] [PubMed]

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M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus, “Deconvolutional networks,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (IEEE, 2010), pp. 2528–2535.



Krizhevsky, A.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems, (2012), pp. 1097–1105.



LeCun, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436 (2015).

[Crossref] [PubMed]

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J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 (2018).



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C. Chen and S. Li, “Valence electron density-dependent pseudopermittivity for nonlocal effects in optical properties of metallic nanoparticles,” ACS Photonics 5, 2295–2304 (2018).

[Crossref]

Li, X.

M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).

[Crossref] [PubMed]

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H. W. Lin, M. Tegmark, and D. Rolnick, “Why does deep and cheap learning work so well?” J. Stat. Phys. 168, 1223–1247 (2017).

[Crossref]

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J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson, “Understanding neural networks through deep visualization,” arXiv preprint arXiv:1506.06579 (2015).



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D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures,” ACS Photonics 5, 1365–1369 (2018).

[Crossref]

Liu, W.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector,” in European Conference on Computer Vision, (2016), pp. 21–37.



Liu, X.

Liu, Y.

W. Ma, F. Cheng, and Y. Liu, “Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials,” ACS Nano 12, 6326–6334 (2018).

[Crossref] [PubMed]

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M. Muja and D. G. Lowe, “Scalable nearest neighbor algorithms for high dimensional data,” IEEE Trans. Pattern Analysis Mach. Intell. 36, 2227–2240 (2014).

[Crossref]

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F. Lussier, D. Missirlis, J. P. Spatz, and J.-F. Masson, “Machine learning driven surface-enhanced raman scattering optophysiology reveals multiplexed metabolite gradients near cells,” ACS Nano 13, 1403–1411 (2019).

[PubMed]

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W. Ma, F. Cheng, and Y. Liu, “Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials,” ACS Nano 12, 6326–6334 (2018).

[Crossref] [PubMed]

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M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).

[Crossref] [PubMed]

Malkiel, I.

I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, and H. Suchowski, “Plasmonic nanostructure design and characterization via Deep Learning,” Light. Sci. Appl. 760 (2018).

[Crossref]

Masson, J.-F.

F. Lussier, D. Missirlis, J. P. Spatz, and J.-F. Masson, “Machine learning driven surface-enhanced raman scattering optophysiology reveals multiplexed metabolite gradients near cells,” ACS Nano 13, 1403–1411 (2019).

[PubMed]

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T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed Representations of Words and Phrases and their Compositionality,” in Advances in Neural Information Processing Systems, (NIPS, 2013), pp. 1–9.



Ming, X.

Missirlis, D.

F. Lussier, D. Missirlis, J. P. Spatz, and J.-F. Masson, “Machine learning driven surface-enhanced raman scattering optophysiology reveals multiplexed metabolite gradients near cells,” ACS Nano 13, 1403–1411 (2019).

[PubMed]

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I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, and H. Suchowski, “Plasmonic nanostructure design and characterization via Deep Learning,” Light. Sci. Appl. 760 (2018).

[Crossref]

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M. Muja and D. G. Lowe, “Scalable nearest neighbor algorithms for high dimensional data,” IEEE Trans. Pattern Analysis Mach. Intell. 36, 2227–2240 (2014).

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I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, and H. Suchowski, “Plasmonic nanostructure design and characterization via Deep Learning,” Light. Sci. Appl. 760 (2018).

[Crossref]

Neshev, D. N.

J. Sautter, I. Staude, M. Decker, E. Rusak, D. N. Neshev, I. Brener, and Y. S. Kivshar, “Active tuning of all-dielectric metasurfaces,” ACS Nano 9, 4308–4315 (2015).

[Crossref] [PubMed]

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J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson, “Understanding neural networks through deep visualization,” arXiv preprint arXiv:1506.06579 (2015).



Nirantar, S.

D. Headland, E. Carrasco, S. Nirantar, W. Withayachumnankul, P. Gutruf, J. Schwarz, D. Abbott, M. Bhaskaran, S. Sriram, J. Perruisseau-Carrier, and C. Fumeaux, “Dielectric resonator reflectarray as high-efficiency nonuniform terahertz metasurface,” ACS Photonics 3, 1019–1026 (2016).

[Crossref]

Nogan, J.

C. C. Chang, L. Huang, J. Nogan, and H. T. Chen, “Invited Article: Narrowband terahertz bandpass filters employing stacked bilayer metasurface antireflection structures,” APL Photonics 3051602 (2018).

[Crossref]

Padilla, W. J.

Parmar, N.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Advances in Neural Information Processing Systems, (NIPS, 2017), Nips.



Perruisseau-Carrier, J.

D. Headland, E. Carrasco, S. Nirantar, W. Withayachumnankul, P. Gutruf, J. Schwarz, D. Abbott, M. Bhaskaran, S. Sriram, J. Perruisseau-Carrier, and C. Fumeaux, “Dielectric resonator reflectarray as high-efficiency nonuniform terahertz metasurface,” ACS Photonics 3, 1019–1026 (2016).

[Crossref]

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J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 44206 (2018).

[Crossref] [PubMed]

Polosukhin, I.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Advances in Neural Information Processing Systems, (NIPS, 2017), Nips.



Qiu, M.

Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljačić, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6, 1168–1174 (2019).

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