Artículos de revistas sobre el tema "Lipschitz neural network"
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Zhu, Zelong, Chunna Zhao y Yaqun Huang. "Fractional order Lipschitz recurrent neural network with attention for long time series prediction". Journal of Physics: Conference Series 2813, n.º 1 (1 de agosto de 2024): 012015. http://dx.doi.org/10.1088/1742-6596/2813/1/012015.
Texto completoZhang, Huan, Pengchuan Zhang y Cho-Jui Hsieh. "RecurJac: An Efficient Recursive Algorithm for Bounding Jacobian Matrix of Neural Networks and Its Applications". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 5757–64. http://dx.doi.org/10.1609/aaai.v33i01.33015757.
Texto completoAraujo, Alexandre, Benjamin Negrevergne, Yann Chevaleyre y Jamal Atif. "On Lipschitz Regularization of Convolutional Layers using Toeplitz Matrix Theory". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 8 (18 de mayo de 2021): 6661–69. http://dx.doi.org/10.1609/aaai.v35i8.16824.
Texto completoXu, Yuhui, Wenrui Dai, Yingyong Qi, Junni Zou y Hongkai Xiong. "Iterative Deep Neural Network Quantization With Lipschitz Constraint". IEEE Transactions on Multimedia 22, n.º 7 (julio de 2020): 1874–88. http://dx.doi.org/10.1109/tmm.2019.2949857.
Texto completoMohammad, Ibtihal J. "Neural Networks of the Rational r-th Powers of the Multivariate Bernstein Operators". BASRA JOURNAL OF SCIENCE 40, n.º 2 (1 de septiembre de 2022): 258–73. http://dx.doi.org/10.29072/basjs.20220201.
Texto completoIbtihal.J.M y Ali J. Mohammad. "Neural Network of Multivariate Square Rational Bernstein Operators with Positive Integer Parameter". European Journal of Pure and Applied Mathematics 15, n.º 3 (31 de julio de 2022): 1189–200. http://dx.doi.org/10.29020/nybg.ejpam.v15i3.4425.
Texto completoLiu, Kanglin y Guoping Qiu. "Lipschitz constrained GANs via boundedness and continuity". Neural Computing and Applications 32, n.º 24 (24 de mayo de 2020): 18271–83. http://dx.doi.org/10.1007/s00521-020-04954-z.
Texto completoOthmani, S., N. E. Tatar y A. Khemmoudj. "Asymptotic behavior of a BAM neural network with delays of distributed type". Mathematical Modelling of Natural Phenomena 16 (2021): 29. http://dx.doi.org/10.1051/mmnp/2021023.
Texto completoXia, Youshen. "An Extended Projection Neural Network for Constrained Optimization". Neural Computation 16, n.º 4 (1 de abril de 2004): 863–83. http://dx.doi.org/10.1162/089976604322860730.
Texto completoLi, Peiluan, Yuejing Lu, Changjin Xu y Jing Ren. "Bifurcation Phenomenon and Control Technique in Fractional BAM Neural Network Models Concerning Delays". Fractal and Fractional 7, n.º 1 (22 de diciembre de 2022): 7. http://dx.doi.org/10.3390/fractalfract7010007.
Texto completoWei Bian y Xiaojun Chen. "Smoothing Neural Network for Constrained Non-Lipschitz Optimization With Applications". IEEE Transactions on Neural Networks and Learning Systems 23, n.º 3 (marzo de 2012): 399–411. http://dx.doi.org/10.1109/tnnls.2011.2181867.
Texto completoChen, Xin, Yujuan Si, Zhanyuan Zhang, Wenke Yang y Jianchao Feng. "Improving Adversarial Robustness of ECG Classification Based on Lipschitz Constraints and Channel Activation Suppression". Sensors 24, n.º 9 (6 de mayo de 2024): 2954. http://dx.doi.org/10.3390/s24092954.
Texto completoZhang, Chi, Wenjie Ruan y Peipei Xu. "Reachability Analysis of Neural Network Control Systems". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 12 (26 de junio de 2023): 15287–95. http://dx.doi.org/10.1609/aaai.v37i12.26783.
Texto completoYu, Hongshan, Jinzhu Peng y Yandong Tang. "Identification of Nonlinear Dynamic Systems Using Hammerstein-Type Neural Network". Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/959507.
Texto completoXin, YU, WU Lingzhen, XIE Mian, WANG Yanlin, XU Liuming, LU Huixia y XU Chenhua. "Smoothing Neural Network for Non‐Lipschitz Optimization with Linear Inequality Constraints". Chinese Journal of Electronics 30, n.º 4 (julio de 2021): 634–43. http://dx.doi.org/10.1049/cje.2021.05.005.
Texto completoZhao, Chunna, Junjie Ye, Zelong Zhu y Yaqun Huang. "FLRNN-FGA: Fractional-Order Lipschitz Recurrent Neural Network with Frequency-Domain Gated Attention Mechanism for Time Series Forecasting". Fractal and Fractional 8, n.º 7 (22 de julio de 2024): 433. http://dx.doi.org/10.3390/fractalfract8070433.
Texto completoLiang, Youwei y Dong Huang. "Large Norms of CNN Layers Do Not Hurt Adversarial Robustness". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 10 (18 de mayo de 2021): 8565–73. http://dx.doi.org/10.1609/aaai.v35i10.17039.
Texto completoZhuo, Li’an, Baochang Zhang, Chen Chen, Qixiang Ye, Jianzhuang Liu y David Doermann. "Calibrated Stochastic Gradient Descent for Convolutional Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 9348–55. http://dx.doi.org/10.1609/aaai.v33i01.33019348.
Texto completoLippl, Samuel, Benjamin Peters y Nikolaus Kriegeskorte. "Can neural networks benefit from objectives that encourage iterative convergent computations? A case study of ResNets and object classification". PLOS ONE 19, n.º 3 (21 de marzo de 2024): e0293440. http://dx.doi.org/10.1371/journal.pone.0293440.
Texto completoFeyzdar, Mahdi, Ahmad Reza Vali y Valiollah Babaeipour. "Identification and Optimization of Recombinant E. coli Fed-Batch Fermentation Producing γ-Interferon Protein". International Journal of Chemical Reactor Engineering 11, n.º 1 (18 de junio de 2013): 123–34. http://dx.doi.org/10.1515/ijcre-2012-0081.
Texto completoStamova, Ivanka, Trayan Stamov y Gani Stamov. "Lipschitz stability analysis of fractional-order impulsive delayed reaction-diffusion neural network models". Chaos, Solitons & Fractals 162 (septiembre de 2022): 112474. http://dx.doi.org/10.1016/j.chaos.2022.112474.
Texto completoChen, Yu-Wen, Ming-Li Chiang y Li-Chen Fu. "Adaptive Formation Control for Multiple Quadrotors with Nonlinear Uncertainties Using Lipschitz Neural Network". IFAC-PapersOnLine 56, n.º 2 (2023): 8714–19. http://dx.doi.org/10.1016/j.ifacol.2023.10.053.
Texto completoLi, Wenjing, Wei Bian y Xiaoping Xue. "Projected Neural Network for a Class of Non-Lipschitz Optimization Problems With Linear Constraints". IEEE Transactions on Neural Networks and Learning Systems 31, n.º 9 (septiembre de 2020): 3361–73. http://dx.doi.org/10.1109/tnnls.2019.2944388.
Texto completoAkrour, Riad, Asma Atamna y Jan Peters. "Convex optimization with an interpolation-based projection and its application to deep learning". Machine Learning 110, n.º 8 (19 de julio de 2021): 2267–89. http://dx.doi.org/10.1007/s10994-021-06037-z.
Texto completoHumphries, Usa, Grienggrai Rajchakit, Pramet Kaewmesri, Pharunyou Chanthorn, Ramalingam Sriraman, Rajendran Samidurai y Chee Peng Lim. "Global Stability Analysis of Fractional-Order Quaternion-Valued Bidirectional Associative Memory Neural Networks". Mathematics 8, n.º 5 (14 de mayo de 2020): 801. http://dx.doi.org/10.3390/math8050801.
Texto completoBensidhoum, Tarek, Farah Bouakrif y Michel Zasadzinski. "Iterative learning radial basis function neural networks control for unknown multi input multi output nonlinear systems with unknown control direction". Transactions of the Institute of Measurement and Control 41, n.º 12 (19 de febrero de 2019): 3452–67. http://dx.doi.org/10.1177/0142331219826659.
Texto completoZhang, Fan, Heng-You Lan y Hai-Yang Xu. "Generalized Hukuhara Weak Solutions for a Class of Coupled Systems of Fuzzy Fractional Order Partial Differential Equations without Lipschitz Conditions". Mathematics 10, n.º 21 (30 de octubre de 2022): 4033. http://dx.doi.org/10.3390/math10214033.
Texto completoLaurel, Jacob, Rem Yang, Shubham Ugare, Robert Nagel, Gagandeep Singh y Sasa Misailovic. "A general construction for abstract interpretation of higher-order automatic differentiation". Proceedings of the ACM on Programming Languages 6, OOPSLA2 (31 de octubre de 2022): 1007–35. http://dx.doi.org/10.1145/3563324.
Texto completoTatar, Nasser-Eddine. "Long Time Behavior for a System of Differential Equations with Non-Lipschitzian Nonlinearities". Advances in Artificial Neural Systems 2014 (14 de septiembre de 2014): 1–7. http://dx.doi.org/10.1155/2014/252674.
Texto completoLi, Jia, Cong Fang y Zhouchen Lin. "Lifted Proximal Operator Machines". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 4181–88. http://dx.doi.org/10.1609/aaai.v33i01.33014181.
Texto completoCantarini, Marco, Lucian Coroianu, Danilo Costarelli, Sorin G. Gal y Gianluca Vinti. "Inverse Result of Approximation for the Max-Product Neural Network Operators of the Kantorovich Type and Their Saturation Order". Mathematics 10, n.º 1 (25 de diciembre de 2021): 63. http://dx.doi.org/10.3390/math10010063.
Texto completoZhao, Liquan y Yan Liu. "Spectral Normalization for Domain Adaptation". Information 11, n.º 2 (27 de enero de 2020): 68. http://dx.doi.org/10.3390/info11020068.
Texto completoPantoja-Garcia, Luis, Vicente Parra-Vega, Rodolfo Garcia-Rodriguez y Carlos Ernesto Vázquez-García. "A Novel Actor—Critic Motor Reinforcement Learning for Continuum Soft Robots". Robotics 12, n.º 5 (9 de octubre de 2023): 141. http://dx.doi.org/10.3390/robotics12050141.
Texto completoVan, Mien. "Higher-order terminal sliding mode controller for fault accommodation of Lipschitz second-order nonlinear systems using fuzzy neural network". Applied Soft Computing 104 (junio de 2021): 107186. http://dx.doi.org/10.1016/j.asoc.2021.107186.
Texto completoJiao, Yulin, Feng Xiao, Wenjuan Zhang, Shujuan Huang, Hao Lu y Zhaoting Lu. "Image Inpainting based on Gated Convolution and spectral Normalization". Frontiers in Computing and Intelligent Systems 6, n.º 2 (5 de diciembre de 2023): 96–100. http://dx.doi.org/10.54097/wkezn917.
Texto completoLi, Cuiying, Rui Wu y Ranzhuo Ma. "Existence of solutions for Caputo fractional iterative equations under several boundary value conditions". AIMS Mathematics 8, n.º 1 (2022): 317–39. http://dx.doi.org/10.3934/math.2023015.
Texto completoTong, Qingbin, Feiyu Lu, Ziwei Feng, Qingzhu Wan, Guoping An, Junci Cao y Tao Guo. "A Novel Method for Fault Diagnosis of Bearings with Small and Imbalanced Data Based on Generative Adversarial Networks". Applied Sciences 12, n.º 14 (21 de julio de 2022): 7346. http://dx.doi.org/10.3390/app12147346.
Texto completoPauli, Patricia, Anne Koch, Julian Berberich, Paul Kohler y Frank Allgower. "Training Robust Neural Networks Using Lipschitz Bounds". IEEE Control Systems Letters 6 (2022): 121–26. http://dx.doi.org/10.1109/lcsys.2021.3050444.
Texto completoNegrini, Elisa, Giovanna Citti y Luca Capogna. "System identification through Lipschitz regularized deep neural networks". Journal of Computational Physics 444 (noviembre de 2021): 110549. http://dx.doi.org/10.1016/j.jcp.2021.110549.
Texto completoZou, Dongmian, Radu Balan y Maneesh Singh. "On Lipschitz Bounds of General Convolutional Neural Networks". IEEE Transactions on Information Theory 66, n.º 3 (marzo de 2020): 1738–59. http://dx.doi.org/10.1109/tit.2019.2961812.
Texto completoLaurel, Jacob, Rem Yang, Gagandeep Singh y Sasa Misailovic. "A dual number abstraction for static analysis of Clarke Jacobians". Proceedings of the ACM on Programming Languages 6, POPL (16 de enero de 2022): 1–30. http://dx.doi.org/10.1145/3498718.
Texto completoGarcía Cabello, Julia. "Mathematical Neural Networks". Axioms 11, n.º 2 (17 de febrero de 2022): 80. http://dx.doi.org/10.3390/axioms11020080.
Texto completoMa, Shuo y Yanmei Kang. "Exponential synchronization of delayed neutral-type neural networks with Lévy noise under non-Lipschitz condition". Communications in Nonlinear Science and Numerical Simulation 57 (abril de 2018): 372–87. http://dx.doi.org/10.1016/j.cnsns.2017.10.012.
Texto completoNeumayer, Sebastian, Alexis Goujon, Pakshal Bohra y Michael Unser. "Approximation of Lipschitz Functions Using Deep Spline Neural Networks". SIAM Journal on Mathematics of Data Science 5, n.º 2 (15 de mayo de 2023): 306–22. http://dx.doi.org/10.1137/22m1504573.
Texto completoSong, Xueli y Jigen Peng. "Global Asymptotic Stability of Impulsive CNNs with Proportional Delays and Partially Lipschitz Activation Functions". Abstract and Applied Analysis 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/832892.
Texto completoHan, Fangfang, Bin Liu, Junchao Zhu y Baofeng Zhang. "Algorithm Design for Edge Detection of High-Speed Moving Target Image under Noisy Environment". Sensors 19, n.º 2 (16 de enero de 2019): 343. http://dx.doi.org/10.3390/s19020343.
Texto completoBecktor, Jonathan, Frederik Schöller, Evangelos Boukas, Mogens Blanke y Lazaros Nalpantidis. "Lipschitz Constrained Neural Networks for Robust Object Detection at Sea". IOP Conference Series: Materials Science and Engineering 929 (27 de noviembre de 2020): 012023. http://dx.doi.org/10.1088/1757-899x/929/1/012023.
Texto completoAziznejad, Shayan, Harshit Gupta, Joaquim Campos y Michael Unser. "Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant". IEEE Transactions on Signal Processing 68 (2020): 4688–99. http://dx.doi.org/10.1109/tsp.2020.3014611.
Texto completoDelaney, Blaise, Nicole Schulte, Gregory Ciezarek, Niklas Nolte, Mike Williams y Johannes Albrecht. "Applications of Lipschitz neural networks to the Run 3 LHCb trigger system". EPJ Web of Conferences 295 (2024): 09005. http://dx.doi.org/10.1051/epjconf/202429509005.
Texto completoMallat, Stéphane, Sixin Zhang y Gaspar Rochette. "Phase harmonic correlations and convolutional neural networks". Information and Inference: A Journal of the IMA 9, n.º 3 (5 de noviembre de 2019): 721–47. http://dx.doi.org/10.1093/imaiai/iaz019.
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