Artículos de revistas sobre el tema "Deep Photonic Neural Networks"
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Pai, Sunil, Zhanghao Sun, Tyler W. Hughes, Taewon Park, Ben Bartlett, Ian A. D. Williamson, Momchil Minkov et al. "Experimentally realized in situ backpropagation for deep learning in photonic neural networks". Science 380, n.º 6643 (28 de abril de 2023): 398–404. http://dx.doi.org/10.1126/science.ade8450.
Texto completoSheng, Huayi. "Review of Integrated Diffractive Deep Neural Networks". Highlights in Science, Engineering and Technology 24 (27 de diciembre de 2022): 264–78. http://dx.doi.org/10.54097/hset.v24i.3957.
Texto completoJiang, Jiaqi y Jonathan A. Fan. "Multiobjective and categorical global optimization of photonic structures based on ResNet generative neural networks". Nanophotonics 10, n.º 1 (22 de septiembre de 2020): 361–69. http://dx.doi.org/10.1515/nanoph-2020-0407.
Texto completoMao, Simei, Lirong Cheng, Caiyue Zhao, Faisal Nadeem Khan, Qian Li y H. Y. Fu. "Inverse Design for Silicon Photonics: From Iterative Optimization Algorithms to Deep Neural Networks". Applied Sciences 11, n.º 9 (23 de abril de 2021): 3822. http://dx.doi.org/10.3390/app11093822.
Texto completoDang, Dharanidhar, Sai Vineel Reddy Chittamuru, Sudeep Pasricha, Rabi Mahapatra y Debashis Sahoo. "BPLight-CNN: A Photonics-Based Backpropagation Accelerator for Deep Learning". ACM Journal on Emerging Technologies in Computing Systems 17, n.º 4 (31 de octubre de 2021): 1–26. http://dx.doi.org/10.1145/3446212.
Texto completoAhmed, Moustafa, Yas Al-Hadeethi, Ahmed Bakry, Hamed Dalir y Volker J. Sorger. "Integrated photonic FFT for photonic tensor operations towards efficient and high-speed neural networks". Nanophotonics 9, n.º 13 (26 de junio de 2020): 4097–108. http://dx.doi.org/10.1515/nanoph-2020-0055.
Texto completoSun, Yichen, Mingli Dong, Mingxin Yu, Jiabin Xia, Xu Zhang, Yuchen Bai, Lidan Lu y Lianqing Zhu. "Nonlinear All-Optical Diffractive Deep Neural Network with 10.6 μm Wavelength for Image Classification". International Journal of Optics 2021 (27 de febrero de 2021): 1–16. http://dx.doi.org/10.1155/2021/6667495.
Texto completoRen, Yangming, Lingxuan Zhang, Weiqiang Wang, Xinyu Wang, Yufang Lei, Yulong Xue, Xiaochen Sun y Wenfu Zhang. "Genetic-algorithm-based deep neural networks for highly efficient photonic device design". Photonics Research 9, n.º 6 (24 de mayo de 2021): B247. http://dx.doi.org/10.1364/prj.416294.
Texto completoAsano, Takashi y Susumu Noda. "Iterative optimization of photonic crystal nanocavity designs by using deep neural networks". Nanophotonics 8, n.º 12 (16 de noviembre de 2019): 2243–56. http://dx.doi.org/10.1515/nanoph-2019-0308.
Texto completoLi, Renjie, Xiaozhe Gu, Yuanwen Shen, Ke Li, Zhen Li y Zhaoyu Zhang. "Smart and Rapid Design of Nanophotonic Structures by an Adaptive and Regularized Deep Neural Network". Nanomaterials 12, n.º 8 (16 de abril de 2022): 1372. http://dx.doi.org/10.3390/nano12081372.
Texto completoShi, Bin, Nicola Calabretta y Ripalta Stabile. "Numerical Simulation of an InP Photonic Integrated Cross-Connect for Deep Neural Networks on Chip". Applied Sciences 10, n.º 2 (9 de enero de 2020): 474. http://dx.doi.org/10.3390/app10020474.
Texto completoSkontranis, Menelaos, George Sarantoglou, Stavros Deligiannidis, Adonis Bogris y Charis Mesaritakis. "Time-Multiplexed Spiking Convolutional Neural Network Based on VCSELs for Unsupervised Image Classification". Applied Sciences 11, n.º 4 (3 de febrero de 2021): 1383. http://dx.doi.org/10.3390/app11041383.
Texto completoHaffner, Christian, Andreas Joerg, Michael Doderer, Felix Mayor, Daniel Chelladurai, Yuriy Fedoryshyn, Cosmin Ioan Roman et al. "Nano–opto-electro-mechanical switches operated at CMOS-level voltages". Science 366, n.º 6467 (14 de noviembre de 2019): 860–64. http://dx.doi.org/10.1126/science.aay8645.
Texto completoShi, Bin, Nicola Calabretta y Ripalta Stabile. "Emulation and modelling of semiconductor optical amplifier-based all-optical photonic integrated deep neural network with arbitrary depth". Neuromorphic Computing and Engineering 2, n.º 3 (1 de septiembre de 2022): 034010. http://dx.doi.org/10.1088/2634-4386/ac8827.
Texto completoChen, Xinyu, Renjie Li, Yueyao Yu, Yuanwen Shen, Wenye Li, Yin Zhang y Zhaoyu Zhang. "POViT: Vision Transformer for Multi-Objective Design and Characterization of Photonic Crystal Nanocavities". Nanomaterials 12, n.º 24 (9 de diciembre de 2022): 4401. http://dx.doi.org/10.3390/nano12244401.
Texto completoHegde, Ravi S. "Photonics Inverse Design: Pairing Deep Neural Networks With Evolutionary Algorithms". IEEE Journal of Selected Topics in Quantum Electronics 26, n.º 1 (enero de 2020): 1–8. http://dx.doi.org/10.1109/jstqe.2019.2933796.
Texto completoShi, Bin, Nicola Calabretta y Ripalta Stabile. "Deep Neural Network Through an InP SOA-Based Photonic Integrated Cross-Connect". IEEE Journal of Selected Topics in Quantum Electronics 26, n.º 1 (enero de 2020): 1–11. http://dx.doi.org/10.1109/jstqe.2019.2945548.
Texto completoHead, Sarah y Mehdi Keshavarz Hedayati. "Inverse Design of Distributed Bragg Reflectors Using Deep Learning". Applied Sciences 12, n.º 10 (11 de mayo de 2022): 4877. http://dx.doi.org/10.3390/app12104877.
Texto completoMeng, Xiangyan, Nuannuan Shi, Guangyi Li, Wei Li, Ninghua Zhu y Ming Li. "Optical Convolutional Neural Networks: Methodology and Advances (Invited)". Applied Sciences 13, n.º 13 (26 de junio de 2023): 7523. http://dx.doi.org/10.3390/app13137523.
Texto completoPanusa, Giulia, Niyazi Ulas Dinc y Demetri Psaltis. "Photonic waveguide bundles using 3D laser writing and deep neural network image reconstruction". Optics Express 30, n.º 2 (11 de enero de 2022): 2564. http://dx.doi.org/10.1364/oe.446775.
Texto completoTu, Xin, Wansheng Xie, Zhenmin Chen, Ming-Feng Ge, Tianye Huang, Chaolong Song y H. Y. Fu. "Analysis of Deep Neural Network Models for Inverse Design of Silicon Photonic Grating Coupler". Journal of Lightwave Technology 39, n.º 9 (1 de mayo de 2021): 2790–99. http://dx.doi.org/10.1109/jlt.2021.3057473.
Texto completoAlagappan, Gandhi, Jun Rong Ong, Zaifeng Yang, Thomas Yong Long Ang, Weijiang Zhao, Yang Jiang, Wenzu Zhang y Ching Eng Png. "Leveraging AI in Photonics and Beyond". Photonics 9, n.º 2 (28 de enero de 2022): 75. http://dx.doi.org/10.3390/photonics9020075.
Texto completoHamerly, Ryan. "The Future of Deep Learning Is Photonic: Reducing the energy needs of neural networks might require computing with light". IEEE Spectrum 58, n.º 7 (julio de 2021): 30–47. http://dx.doi.org/10.1109/mspec.2021.9475393.
Texto completoLi, Caiyun, Jiangyong He, Yange Liu, Yang Yue, Luhe Zhang, Longfei Zhu, Mengjie Zhou, Congcong Liu, Kaiyan Zhu y Zhi Wang. "Comparing Performance of Deep Convolution Networks in Reconstructing Soliton Molecules Dynamics from Real-Time Spectral Interference". Photonics 8, n.º 2 (13 de febrero de 2021): 51. http://dx.doi.org/10.3390/photonics8020051.
Texto completoZhou, Yuewen, Fangzheng Zhang, Jingzhan Shi y Shilong Pan. "Deep neural network-assisted high-accuracy microwave instantaneous frequency measurement with a photonic scanning receiver". Optics Letters 45, n.º 11 (27 de mayo de 2020): 3038. http://dx.doi.org/10.1364/ol.391883.
Texto completoMirsu, Radu, Georgiana Simion, Catalin Daniel Caleanu y Ioana Monica Pop-Calimanu. "A PointNet-Based Solution for 3D Hand Gesture Recognition". Sensors 20, n.º 11 (5 de junio de 2020): 3226. http://dx.doi.org/10.3390/s20113226.
Texto completoVillegas Burgos, Carlos Mauricio y Nickolas Vamivakas. "Challenges in the Path Toward a Scalable Silicon Photonics Implementation of Deep Neural Networks". IEEE Journal of Quantum Electronics 55, n.º 5 (octubre de 2019): 1–10. http://dx.doi.org/10.1109/jqe.2019.2934758.
Texto completoLi, Fengrong, Yifan Sun y XiangDong Zhang. "Deep-learning-based quantum imaging using NOON states". Journal of Physics Communications 6, n.º 3 (1 de marzo de 2022): 035005. http://dx.doi.org/10.1088/2399-6528/ac5e25.
Texto completoYao, Kan, Rohit Unni y Yuebing Zheng. "Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale". Nanophotonics 8, n.º 3 (25 de enero de 2019): 339–66. http://dx.doi.org/10.1515/nanoph-2018-0183.
Texto completoAi, Xiaocong, Shih-Chieh Hsu, Ke Li y Chih-Ting Lu. "Probing highly collimated photon-jets with deep learning". Journal of Physics: Conference Series 2438, n.º 1 (1 de febrero de 2023): 012114. http://dx.doi.org/10.1088/1742-6596/2438/1/012114.
Texto completoVlimant, Jean-Roch, Felice Pantaleo, Maurizio Pierini, Vladimir Loncar, Sofia Vallecorsa, Dustin Anderson, Thong Nguyen y Alexander Zlokapa. "Large-Scale Distributed Training Applied to Generative Adversarial Networks for Calorimeter Simulation". EPJ Web of Conferences 214 (2019): 06025. http://dx.doi.org/10.1051/epjconf/201921406025.
Texto completoMassari, Luca, Giulia Fransvea, Jessica D’Abbraccio, Mariangela Filosa, Giuseppe Terruso, Andrea Aliperta, Giacomo D’Alesio et al. "Functional mimicry of Ruffini receptors with fibre Bragg gratings and deep neural networks enables a bio-inspired large-area tactile-sensitive skin". Nature Machine Intelligence 4, n.º 5 (mayo de 2022): 425–35. http://dx.doi.org/10.1038/s42256-022-00487-3.
Texto completoNakadai, Masahiro, Kengo Tanaka, Takashi Asano, Yasushi Takahashi y Susumu Noda. "Statistical evaluation of Q factors of fabricated photonic crystal nanocavities designed by using a deep neural network". Applied Physics Express 13, n.º 1 (3 de diciembre de 2019): 012002. http://dx.doi.org/10.7567/1882-0786/ab5978.
Texto completoZhao, Zeyu, Jie You, Jun Zhang y Yuhua Tang. "Data-Enhanced Deep Greedy Optimization Algorithm for the On-Demand Inverse Design of TMDC-Cavity Heterojunctions". Nanomaterials 12, n.º 17 (28 de agosto de 2022): 2976. http://dx.doi.org/10.3390/nano12172976.
Texto completoGan, Linqiao, Fei Yu, Yazhou Wang, Ning Wang, Xinyue Zhu, Lili Hu y Chunlei Yu. "Dispersion-Oriented Inverse Design of Photonic-Crystal Fiber for Four-Wave Mixing Application". Photonics 10, n.º 3 (10 de marzo de 2023): 294. http://dx.doi.org/10.3390/photonics10030294.
Texto completoXie, Tangyao y Jianguo Yu. "4Gbaud PS-16QAM D-Band Fiber-Wireless Transmission Over 4.6 km by Using Balance Complex-Valued NN Equalizer with Random Oversampling". Sensors 23, n.º 7 (31 de marzo de 2023): 3655. http://dx.doi.org/10.3390/s23073655.
Texto completoShinde, Ashwini y Prof Madhav Ingle. "Hybrid Approach for Skin Disease Classification: Integrating Machine learning and Deep Learning". International Journal for Research in Applied Science and Engineering Technology 11, n.º 5 (31 de mayo de 2023): 3243–48. http://dx.doi.org/10.22214/ijraset.2023.52338.
Texto completoReddy, G. Mahesh, P. Hema Venkata Ramana, Ponnuru Anusha, Battula Kalyan Chakravarthy, Aravinda Kasukurthi y Vaddempudi Sujatha Lakshmi. "A Survey on Sugarcane Leaf Disease Identification Using Deep Learning Technique(CNN)". International Journal on Recent and Innovation Trends in Computing and Communication 11, n.º 5 (17 de mayo de 2023): 248–54. http://dx.doi.org/10.17762/ijritcc.v11i5.6611.
Texto completoValsecchi, Davide. "Deep learning techniques for energy clustering in the CMS ECAL". Journal of Physics: Conference Series 2438, n.º 1 (1 de febrero de 2023): 012077. http://dx.doi.org/10.1088/1742-6596/2438/1/012077.
Texto completoOrban de Xivry, G., M. Quesnel, P.-O. Vanberg, O. Absil y G. Louppe. "Focal plane wavefront sensing using machine learning: performance of convolutional neural networks compared to fundamental limits". Monthly Notices of the Royal Astronomical Society 505, n.º 4 (9 de junio de 2021): 5702–13. http://dx.doi.org/10.1093/mnras/stab1634.
Texto completoWoods, Damien y Thomas J. Naughton. "Photonic neural networks". Nature Physics 8, n.º 4 (abril de 2012): 257–59. http://dx.doi.org/10.1038/nphys2283.
Texto completoBrunner, Daniel y Demetri Psaltis. "Competitive photonic neural networks". Nature Photonics 15, n.º 5 (30 de abril de 2021): 323–24. http://dx.doi.org/10.1038/s41566-021-00803-0.
Texto completoYan, Ye-Peng, Guo-Jian Wang, Si-Yu Li y Jun-Qing Xia. "Delensing of Cosmic Microwave Background Polarization with Machine Learning". Astrophysical Journal Supplement Series 267, n.º 1 (27 de junio de 2023): 2. http://dx.doi.org/10.3847/1538-4365/acd2ce.
Texto completoDe Marinis, Lorenzo, Marco Cococcioni, Piero Castoldi y Nicola Andriolli. "Photonic Neural Networks: A Survey". IEEE Access 7 (2019): 175827–41. http://dx.doi.org/10.1109/access.2019.2957245.
Texto completoSunny, Febin P., Ebadollah Taheri, Mahdi Nikdast y Sudeep Pasricha. "A Survey on Silicon Photonics for Deep Learning". ACM Journal on Emerging Technologies in Computing Systems 17, n.º 4 (30 de junio de 2021): 1–57. http://dx.doi.org/10.1145/3459009.
Texto completoPadilla-Zepeda, Efrain, Deni Torres-Roman y Andres Mendez-Vazquez. "A Semantic Segmentation Framework for Hyperspectral Imagery Based on Tucker Decomposition and 3DCNN Tested with Simulated Noisy Scenarios". Remote Sensing 15, n.º 5 (1 de marzo de 2023): 1399. http://dx.doi.org/10.3390/rs15051399.
Texto completoSun, Miao, Shenglong Zhuo y Patrick Yin Chiang. "Multi-Scale Histogram-Based Probabilistic Deep Neural Network for Super-Resolution 3D LiDAR Imaging". Sensors 23, n.º 1 (30 de diciembre de 2022): 420. http://dx.doi.org/10.3390/s23010420.
Texto completoStark, Pascal, Folkert Horst, Roger Dangel, Jonas Weiss y Bert Jan Offrein. "Opportunities for integrated photonic neural networks". Nanophotonics 9, n.º 13 (10 de agosto de 2020): 4221–32. http://dx.doi.org/10.1515/nanoph-2020-0297.
Texto completoFarhat, N. H. "Photonic neural networks and learning machines". IEEE Expert 7, n.º 5 (octubre de 1992): 63–72. http://dx.doi.org/10.1109/64.163674.
Texto completoDemirkiran, Cansu, Furkan Eris, Gongyu Wang, Jonathan Elmhurst, Nick Moore, Nicholas C. Harris, Ayon Basumallik, Vijay Janapa Reddi, Ajay Joshi y Darius Bunandar. "An Electro-Photonic System for Accelerating Deep Neural Networks". ACM Journal on Emerging Technologies in Computing Systems, 12 de julio de 2023. http://dx.doi.org/10.1145/3606949.
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