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Статті в журналах з теми "ReLU neural networks"
Liang, XingLong, and Jun Xu. "Biased ReLU neural networks." Neurocomputing 423 (January 2021): 71–79. http://dx.doi.org/10.1016/j.neucom.2020.09.050.
Повний текст джерелаHuang, Changcun. "ReLU Networks Are Universal Approximators via Piecewise Linear or Constant Functions." Neural Computation 32, no. 11 (November 2020): 2249–78. http://dx.doi.org/10.1162/neco_a_01316.
Повний текст джерелаKulathunga, Nalinda, Nishath Rajiv Ranasinghe, Daniel Vrinceanu, Zackary Kinsman, Lei Huang, and Yunjiao Wang. "Effects of Nonlinearity and Network Architecture on the Performance of Supervised Neural Networks." Algorithms 14, no. 2 (February 5, 2021): 51. http://dx.doi.org/10.3390/a14020051.
Повний текст джерелаDung, D., V. K. Nguyen, and M. X. Thao. "ON COMPUTATION COMPLEXITY OF HIGH-DIMENSIONAL APPROXIMATION BY DEEP ReLU NEURAL NETWORKS." BULLETIN of L.N. Gumilyov Eurasian National University. MATHEMATICS. COMPUTER SCIENCE. MECHANICS Series 133, no. 4 (2020): 8–18. http://dx.doi.org/10.32523/2616-7182/2020-133-4-8-18.
Повний текст джерелаGühring, Ingo, Gitta Kutyniok, and Philipp Petersen. "Error bounds for approximations with deep ReLU neural networks in Ws,p norms." Analysis and Applications 18, no. 05 (September 19, 2019): 803–59. http://dx.doi.org/10.1142/s0219530519410021.
Повний текст джерелаDũng, Dinh, Van Kien Nguyen, and Mai Xuan Thao. "COMPUTATION COMPLEXITY OF DEEP RELU NEURAL NETWORKS IN HIGH-DIMENSIONAL APPROXIMATION." Journal of Computer Science and Cybernetics 37, no. 3 (September 28, 2021): 291–320. http://dx.doi.org/10.15625/1813-9663/37/3/15902.
Повний текст джерелаПолковникова, Н. А., Е. В. Тузинкевич, and А. Н. Попов. "Application of convolutional neural networks for monitoring of marine objects." MORSKIE INTELLEKTUAL`NYE TEHNOLOGII), no. 4(50) (December 17, 2020): 53–61. http://dx.doi.org/10.37220/mit.2020.50.4.097.
Повний текст джерелаGao, Hongyang, Lei Cai, and Shuiwang Ji. "Adaptive Convolutional ReLUs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3914–21. http://dx.doi.org/10.1609/aaai.v34i04.5805.
Повний текст джерелаPetzka, Henning, Martin Trimmel, and Cristian Sminchisescu. "Notes on the Symmetries of 2-Layer ReLU-Networks." Proceedings of the Northern Lights Deep Learning Workshop 1 (February 6, 2020): 6. http://dx.doi.org/10.7557/18.5150.
Повний текст джерелаZheng, Shuxin, Qi Meng, Huishuai Zhang, Wei Chen, Nenghai Yu, and Tie-Yan Liu. "Capacity Control of ReLU Neural Networks by Basis-Path Norm." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5925–32. http://dx.doi.org/10.1609/aaai.v33i01.33015925.
Повний текст джерелаДисертації з теми "ReLU neural networks"
Wang, Hao. "A new scheme for training ReLU-based multi-layer feedforward neural networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217384.
Повний текст джерелаEtt nytt schema för träning av rektifierad linjär enhet (ReLU)-baserade och framkopplade neurala nätverk undersöks i denna avhandling. Projektet börjar med en rad-för-rad-uppdateringsstrategi designad för framkopplade neurala nätverk med ett dolt lager (SLFNs). Denna strategi utnyttjar egenskaper i ReLUs och optimerar varje rad i inmatningsviktmatrisen individuellt, enligt en gemensam optimeringsmetod. Därefter föreslås den direkta uppdateringsstrategin (DUS), som har två olika versioner: vektorbaserad metod (VBM) respektive matrisbaserad metod (MBM), för att optimera ingångsviktmatrisen som helhet. Slutli- gen utvidgas DUS till framkopplade neurala nätverk med flera lager (MLFN). Eftersom utvidgningen för generella ReLU-baserade MLFN står inför ett initieringsdilemma presenteras därför en MLFN med en speciell struktur. Verifieringsexperiment utförs på sex datamängder för klassificering av flera klasser. Resultaten bekräftar att MBM-algoritmen för SLFN förbättrar prestanda hos neurala nätverk, jämfört med konkurrenten, den regulariserade extrema inlärningsmaskinen. För de flesta använda dataset, fungerar MLFNs med den föreslagna speciella strukturen bättre när man lägger till extra dolda lager.
Strandqvist, Jonas. "Attractors of autoencoders : Memorization in neural networks." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-97746.
Повний текст джерелаNery, Kaio Cesar Mendes da Silva. "Redu??o do n?mero de parcelas para modelagem da prognose do volume de floresta." UFVJM, 2016. http://acervo.ufvjm.edu.br/jspui/handle/1/1067.
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O objetivo deste estudo foi avaliar o efeito da redu??o de parcelas permanentes no custo da realiza??o do invent?rio florestal cont?nuo e realizar a prognose do volume de floresta comparando o emprego das Redes Neurais Artificiais ao modelo tradicionalmente utilizado proposto por Clutter (1963). Os dados utilizados foram provenientes de povoamentos localizados no litoral norte da Bahia, totalizando cerca de 3.000 hectares de floresta. Foram propostas duas metodologias para auxiliar na redu??o das parcelas. Para a metodologia proposta no estudo 1, os dados foram divididos aleatoriamente em dois grupos: treinamento (10, 20, 30, 40, 50, 60, 70, 80, e 90%) e generaliza??o (90, 80, 70, 60, 50, 40, 30, 20, 10%). Os dados do treinamento foram utilizados para gerar as redes neurais artificias enquanto que os dados da generaliza??o serviram para validar a capacidade das redes em gerar resultados precisos para dados desconhecidos. A metodologia proposta no estudo 2 dividiu aleatoriamente os dados em dois grupos: treinamento a escolha fixa de quantidades de parcelas pr?-estabelecidas nas tr?s classes de s?tio (10, 20, 30, 40, 50 e 60) totalizando 30, 60, 90, 120, 150 e 180 parcelas utilizadas para o treino das redes neurais e os demais dados foram utilizados para validar a capacidade das redes. A estimativa da vari?vel de estudo foi gerada no sistema computacional Neuroforest 3.3. A aplica??o das Redes Neurais Artificiais apresentaram resultados satisfat?rios bem como a aplica??o de ambas metodologias permitiram reduzir consideravelmente o custo para a realiza??o do invent?rio florestal.
Disserta??o (Mestrado) ? Programa de P?s-Gradua??o em Ci?ncia Florestal, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2016.
This study aimed to evaluate the effects of permanent plots in the cost of carrying out the continuous forest inventory and to perform prognosis of forest production comparing the use of Artificial Neural Networks to the traditional model proposed by Clutter (1963). Data were obtained from municipalities located in the northern region of Bahia state yielding 3,000 hectares of forest. Two different methodologies were proposed to reduce the number of parcels. The methodology proposed for the study 1 involved the random division of the data into two groups consisting of random reducing portions of 10, 20, 30, 40, 50, 60, 70, 80, and 90% with these percentages used for training and the remaining 90, 80, 70, 60, 50, 40, 30, 20, 10% used for validation. The methodology proposed for study 2 consisted of random reduction in parcels with a fixed parcel per site 10, 20, 30, 40, 50 and 60 in each class (30, 60, 90, 120, 150 and 180 parcels). The estimates of the study variables were generated in the computer system Neuroforest 3.3. The applications of Artificial Neural Networks showed satisfactory results and the application of both methodologies have considerably reduced the cost for conducting the forest inventory.
Segatto, Ênio Carlos. "Relé diferencial para transformadores de potência utilizando ferramentas inteligentes." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-19022016-144637/.
Повний текст джерелаThis work proposes a complete differential protection system for power transformers, applying the Artificial Neural Network (ANN) theory. The proposed approach treat the classification of the protection system as a problem of pattern recognition and as an alternative method to the conventional algorithms. Several factors such as, for example, transformer energization and CT saturation can cause an inadequate operation of the protection relay. With the objective of improving the power transformer digital protection, a complete protection system was developed, including an ANN-based device in substitution to harmonic filters, in use in the conventional algorithm. Some approaches concerning the reconstruction of the distorted signals caused by the CTs saturation are also proposed. These routines are added to the final protection algorithm and they are compared to the conventional algorithm for power transformer protection. With the use of artificial intelligence tools in a complete power transformer protection algorithm, one intends to obtain a very precise, fast and efficient solution, if compared to the conventional methods.
Penido, Tamires Mousslech Andrade. "Modelagem da produ??o de povoamentos de eucalipto utilizando diferentes metodologias." UFVJM, 2017. http://acervo.ufvjm.edu.br/jspui/handle/1/1460.
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Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES)
A modelagem ? um procedimento estat?stico empregado por gestores florestais para esbo?ar o desenvolvimento vegetal com precis?o. Informa??es confi?veis do crescimento e da produ??o s?o essenciais para predizer e quantificar a estrutura futura do povoamento. O presente trabalho foi dividido em dois cap?tulos. Os objetivos foram avaliar a efici?ncia de se estimar a altura empregando diferentes modelos hipsom?tricos, crit?rios de estratifica??o e m?todos de ajuste, al?m de comparar tr?s categorias de modelos de crescimento e produ??o (MCP) em planta??es comerciais de eucalipto. Foram definidas quatro unidades de manejo florestal, totalizando 293,43 ha. O invent?rio florestal cont?nuo foi realizado em 34 parcelas permanentes de 400 m2. O espa?amento de plantio foi de 3,0 x 2,5 m. Avaliou-se a precis?o do ajuste de treze modelos hipsom?tricos. Foram treinadas RNA empregando as mesmas vari?veis de resposta e preditoras adotadas nas equa??es selecionadas. As categorias de MCP testadas foram: em n?vel de povoamento (MP), pelo sistema de equa??es simult?neas de Clutter; de distribui??o diam?trica (MDD), pelo ajuste de fun??o densidade de probabilidade de Weibul-2P e de ?rvores individuais (MAI), pelo modelo de Pienaar e Schiver. As equa??es provenientes do modelo de altura em fun??o do di?metro e da altura dominante forneceram estimativas confi?veis da altura para diferentes crit?rios de estratifica??o, demonstrando superioridade em rela??o aos modelos locais. A modelagem por regress?o e redes demonstraram-se adequadas para estimar a altura, com ou sem estratifica??o do banco de dados. A estratifica??o ? um procedimento que pode melhorar a qualidade das estimativas de altura obtidas por regress?o e RNA. As tr?s categorias de modelo proporcionaram estimativas confi?veis da produ??o em volume com casca, aos 36, 48, 60 e 72 meses, para as unidades de manejo estudadas. O MAI foi a categoria mais precisa e consistente na estimativa do volume por hectare. As proje??es com MP e MDD podem gerar estimativas similares de volume para idades al?m daquelas em que se realizou o invent?rio florestal.
Disserta??o (Mestrado) ? Programa de P?s-Gradua??o em Ci?ncia Florestal, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2017.
Modeling is a statistical procedure employed by forest managers to sketch plant development with precision. Reliable growth and production information are essential to predict and quantify the future stand structure. The present work was divided in two chapters. The objectives were to evaluate the efficiency of height estimation using different hypsometric models, stratification criteria and adjustment methods, beside to evaluate and compare three categories of growth and yield models (MCP) in commercial eucalypt plantations. Four forest management units were defined, totaling 293.43 ha. The continuous forest inventory was realized in 34 permanent plots of 400 m2. The planting spacing was 3.0 x 2.5 m. The accuracy of the fit of thirteen hypsometric models was evaluated. ANN were trained using the same response e predictive variables adopted in the selected equations. The MCP categories tested were: in level of stand (MP), using Clutter?s simultaneous equations; diameter distribution model (MDD), by adjustment of the Weibull-2P?s probability density function and individual trees (MAI), by Pienaar and Schiver model. The equations from the height model according to the diameter and the dominant height provided reliable height estimates for different stratification criteria, showing superiority in relation to local models. Regression and networks modelling were suitable for estimating height, with or without stratification of the database. Stratification is a procedure that can improve the quality of the estimates obtained by regression and ANN. The three model categories provided reliable estimates of the volume with bark production at 36, 48, 60 and 72 months for the management units studied. MAI was the most accurate and consistent category in estimating volume per hectare. Projections with MP and MDD can generate similar estimates of volume for ages beyond those in which the forest inventory was carried out.
Silva, Lazaro Eduardo da. "Tecnologias WEB aplicadas aos sistemas elétricos de potência." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/18/18154/tde-04102010-153801/.
Повний текст джерелаThe use of digital equipment and data communication for the connection of the devices in an electric power substation provides the implementation of new technologies to improve and upgrade the potentialities of control and supervision of an electrical power system. The utilities demand high investments in the establishment and implementation of supervision, control and data acquisition systems. Based on these systems, it is possible to visualize a map of the electrical network, the installation points of digital relays and the status of their functions, making it possible the diagnosis and control of such equipment. The World Wide Web started a convenient access to information due to the friendly interface with the user. The implementation of a supervising and control system using free web technologies can, in a competitive cost, join data which are geographically available to an ergonomic computational platform and also provides access to the users from any device connected to the web. As a result of this work, a supervision Web control System of a typical bay with a simple interface was set up, as well as the development of reports and announcement of events. A second network supervision system for cutting load and for power reestablishment, known as regional load relief scheme, was implemented. Finally, a voltage and frequency data acquisition system from a power quality equipment with time varying graphics was developed as well as a implementation of a multiple neural perception net (Time Delay Neural Network) in order to predict future values of the frequency. The supervision and control applications were developed with web technologies and tested using an intranet in order assure the application on the electrical power system, which can be accessed by a standard browser. It is relevant to mention that the whole system was tested in actual equipment which belongs to a laboratorial structure available to the post graduation students at the University of São Paulo, School of Engineering of São Carlos, Power Systems Laboratory where two substations with commercial digital relays are simulated and interconnected by a fiber optics backbone and also connected to the local laboratory network which allows remote access to the equipment. Such system as set up in a web platform that can add resources which are distributed in the development of a friendly and intuitive interface in addition to the availability of access to the information from any point connected to the web.
Njima, Wafa. "Méthodes de localisation de capteurs dans le contexte de l'Internet des Objets." Electronic Thesis or Diss., Paris, CNAM, 2019. http://www.theses.fr/2019CNAM1264.
Повний текст джерелаWith the growing emergence of the Internet of Things and the importance of position information in this context, localization is attracting more and more attention in the researchers' community. The outdoor location is provided by GPS which is not suitable for indoors environments. Several indoor localization techniques exist, but there is not yet a standard.Existing methods are mainly based on trilateration or fingerprinting. Trilateration is a geometric method that exploits thedistances between an object and reference points to locate it. This method only works when we have at least 3 access points detected and is strongly affected by multi paths. In order to overcome these disadvantages, the fingerprinting methodcompares the fingerprint associated to the object to be located to a fingerprints' database constructed on offline. The estimated position is a combination of the selected training positions. This method is of great interest. However, it requiressignificant computing and storage capabilities. The aim of this thesis is to improve the existing localization techniqueswhile maintaining a satisfying localization accuracy with low computational complexity. In order to overcome the disadvantages of these two classes of localization techniques, we propose alternative approaches. For trilateration, it hasbeen combined with an optimization process that aims at completing the inter-node distance matrix from partially knowndata. Advanced optimization algorithms have been used in developing the mathematical equation corresponding to eachone. Using this method, we came up with a localization solution for a distributed IoT architecture. As for fingerprinting, we have exploited it to develop localization systems for a centralized IoT architecture. A comparative study between different metrics of similarity evaluation is conducted. This study was followed by the development of a linear model generating a mathematical relation that links the powers of the signal received by an object to its coordinates. This helps to reduce the online complexity of and adapts our system to real time. This is also ensured by the development of a CNN model which deal with the localization problem as radio images classification problem. The performances of all proposed approaches are evaluated and discussed. These results show the improvement of the performances of basic techniques in terms of localization accuracy and complexity
Alsubaihi, Salman. "Studying Perturbations on the Input of Two-Layer Neural Networks with ReLU Activation." Thesis, 2019. http://hdl.handle.net/10754/655886.
Повний текст джерелаBibi, Adel. "Understanding a Block of Layers in Deep Neural Networks: Optimization, Probabilistic and Tropical Geometric Perspectives." Diss., 2020. http://hdl.handle.net/10754/662589.
Повний текст джерелаЧастини книг з теми "ReLU neural networks"
Cui, Jia-le, Shuang Qiu, Ming-yang Jiang, Zhi-li Pei, and Yi-nan Lu. "Text Classification Based on ReLU Activation Function of SAE Algorithm." In Advances in Neural Networks - ISNN 2017, 44–50. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59072-1_6.
Повний текст джерелаHagiwara, Katsuyuki. "On a Fitting of a Heaviside Function by Deep ReLU Neural Networks." In Neural Information Processing, 59–69. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04167-0_6.
Повний текст джерелаBak, Stanley, Hoang-Dung Tran, Kerianne Hobbs, and Taylor T. Johnson. "Improved Geometric Path Enumeration for Verifying ReLU Neural Networks." In Computer Aided Verification, 66–96. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53288-8_4.
Повний текст джерелаPonomarchuk, Anton, Christoph Koutschan, and Bernhard Moser. "Unboundedness of Linear Regions of Deep ReLU Neural Networks." In Communications in Computer and Information Science, 3–10. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14343-4_1.
Повний текст джерелаGoubault, Eric, Sébastien Palumby, Sylvie Putot, Louis Rustenholz, and Sriram Sankaranarayanan. "Static Analysis of ReLU Neural Networks with Tropical Polyhedra." In Static Analysis, 166–90. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88806-0_8.
Повний текст джерелаKleine Büning, Marko, Philipp Kern, and Carsten Sinz. "Verifying Equivalence Properties of Neural Networks with ReLU Activation Functions." In Lecture Notes in Computer Science, 868–84. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58475-7_50.
Повний текст джерелаBak, Stanley. "nnenum: Verification of ReLU Neural Networks with Optimized Abstraction Refinement." In Lecture Notes in Computer Science, 19–36. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76384-8_2.
Повний текст джерелаTran, Hoang-Dung, Neelanjana Pal, Patrick Musau, Diego Manzanas Lopez, Nathaniel Hamilton, Xiaodong Yang, Stanley Bak, and Taylor T. Johnson. "Robustness Verification of Semantic Segmentation Neural Networks Using Relaxed Reachability." In Computer Aided Verification, 263–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_12.
Повний текст джерелаHashemi, Vahid, Panagiotis Kouvaros, and Alessio Lomuscio. "OSIP: Tightened Bound Propagation for the Verification of ReLU Neural Networks." In Software Engineering and Formal Methods, 463–80. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92124-8_26.
Повний текст джерелаKhedr, Haitham, James Ferlez, and Yasser Shoukry. "PEREGRiNN: Penalized-Relaxation Greedy Neural Network Verifier." In Computer Aided Verification, 287–300. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_13.
Повний текст джерелаТези доповідей конференцій з теми "ReLU neural networks"
Moser, Bernhard A., Michal Lewandowski, Somayeh Kargaran, Werner Zellinger, Battista Biggio, and Christoph Koutschan. "Tessellation-Filtering ReLU Neural Networks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/463.
Повний текст джерелаKouvaros, Panagiotis, and Alessio Lomuscio. "Towards Scalable Complete Verification of Relu Neural Networks via Dependency-based Branching." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/364.
Повний текст джерелаAftab, Arya, Alireza Morsali, and Shahrokh Ghaemmaghami. "Multi-Head Relu Implicit Neural Representation Networks." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747352.
Повний текст джерелаSalman, Shaeke, Canlin Zhang, Xiuwen Liu, and Washington Mio. "Towards Quantifying Intrinsic Generalization of Deep ReLU Networks." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206619.
Повний текст джерелаZhu, Yiwei, Feng Wang, Wenjie Wan, and Min Zhang. "Attack-Guided Efficient Robustness Verification of ReLU Neural Networks." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9534410.
Повний текст джерелаWang, Yuan. "Estimation and Comparison of Linear Regions for ReLU Networks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/492.
Повний текст джерелаDaróczy, Bálint, and Dániel Rácz. "Gradient representations in ReLU networks as similarity functions." In ESANN 2021 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2021. http://dx.doi.org/10.14428/esann/2021.es2021-153.
Повний текст джерелаVoigtlaender, Felix, and Philipp Petersen. "Approximation in Lp(µ) with deep ReLU neural networks." In 2019 13th International conference on Sampling Theory and Applications (SampTA). IEEE, 2019. http://dx.doi.org/10.1109/sampta45681.2019.9030992.
Повний текст джерелаIde, Hidenori, and Takio Kurita. "Improvement of learning for CNN with ReLU activation by sparse regularization." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966185.
Повний текст джерелаLi, Gordon H. Y., Ryoto Sekine, Rajveer Nehra, Robert M. Gray, Luis Ledezma, Qiushi Guo, and Alireza Marandi. "All-optical, ultrafast energy-efficient ReLU function for nanophotonic neural networks." In CLEO: Science and Innovations. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/cleo_si.2022.sth5g.6.
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