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Artykuły w czasopismach na temat "Adaptive gradient methods with momentum"
Abdulkadirov, R. I., i P. A. Lyakhov. "A new approach to training neural networks using natural gradient descent with momentum based on Dirichlet distributions". Computer Optics 47, nr 1 (luty 2023): 160–69. http://dx.doi.org/10.18287/2412-6179-co-1147.
Pełny tekst źródłaREHMAN, MUHAMMAD ZUBAIR, i NAZRI MOHD NAWI. "STUDYING THE EFFECT OF ADAPTIVE MOMENTUM IN IMPROVING THE ACCURACY OF GRADIENT DESCENT BACK PROPAGATION ALGORITHM ON CLASSIFICATION PROBLEMS". International Journal of Modern Physics: Conference Series 09 (styczeń 2012): 432–39. http://dx.doi.org/10.1142/s201019451200551x.
Pełny tekst źródłaChen, Ruijuan, Xiaoquan Tang i Xiuting Li. "Adaptive Stochastic Gradient Descent Method for Convex and Non-Convex Optimization". Fractal and Fractional 6, nr 12 (29.11.2022): 709. http://dx.doi.org/10.3390/fractalfract6120709.
Pełny tekst źródłaZhang, Yue, Seong-Yoon Shin, Xujie Tan i Bin Xiong. "A Self-Adaptive Approximated-Gradient-Simulation Method for Black-Box Adversarial Sample Generation". Applied Sciences 13, nr 3 (18.01.2023): 1298. http://dx.doi.org/10.3390/app13031298.
Pełny tekst źródłaLong, Sheng, Wei Tao, Shuohao LI, Jun Lei i Jun Zhang. "On the Convergence of an Adaptive Momentum Method for Adversarial Attacks". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 13 (24.03.2024): 14132–40. http://dx.doi.org/10.1609/aaai.v38i13.29323.
Pełny tekst źródłaZhang, Jiahui, Xinhao Yang, Ke Zhang i Chenrui Wen. "An Adaptive Deep Learning Optimization Method Based on Radius of Curvature". Computational Intelligence and Neuroscience 2021 (10.11.2021): 1–10. http://dx.doi.org/10.1155/2021/9882068.
Pełny tekst źródłaZang, Yu, Zhe Xue, Shilong Ou, Lingyang Chu, Junping Du i Yunfei Long. "Efficient Asynchronous Federated Learning with Prospective Momentum Aggregation and Fine-Grained Correction". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 15 (24.03.2024): 16642–50. http://dx.doi.org/10.1609/aaai.v38i15.29603.
Pełny tekst źródłaLiu, Miaomiao, Dan Yao, Zhigang Liu, Jingfeng Guo i Jing Chen. "An Improved Adam Optimization Algorithm Combining Adaptive Coefficients and Composite Gradients Based on Randomized Block Coordinate Descent". Computational Intelligence and Neuroscience 2023 (10.01.2023): 1–14. http://dx.doi.org/10.1155/2023/4765891.
Pełny tekst źródłaJiang, Shuoran, Qingcai Chen, Youcheng Pan, Yang Xiang, Yukang Lin, Xiangping Wu, Chuanyi Liu i Xiaobao Song. "ZO-AdaMU Optimizer: Adapting Perturbation by the Momentum and Uncertainty in Zeroth-Order Optimization". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 16 (24.03.2024): 18363–71. http://dx.doi.org/10.1609/aaai.v38i16.29796.
Pełny tekst źródłaSineglazov, Victor, i Anatoly Kot. "Design of hybrid neural networks of the ensemble structure". Eastern-European Journal of Enterprise Technologies 1, nr 4 (109) (26.02.2021): 31–45. http://dx.doi.org/10.15587/1729-4061.2021.225301.
Pełny tekst źródłaRozprawy doktorskie na temat "Adaptive gradient methods with momentum"
Barakat, Anas. "Contributions to non-convex stochastic optimization and reinforcement learning". Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT030.
Pełny tekst źródłaThis thesis is focused on the convergence analysis of some popular stochastic approximation methods in use in the machine learning community with applications to optimization and reinforcement learning.The first part of the thesis is devoted to a popular algorithm in deep learning called ADAM used for training neural networks. This variant of stochastic gradient descent is more generally useful for finding a local minimizer of a function. Assuming that the objective function is differentiable and non-convex, we establish the convergence of the iterates in the long run to the set of critical points under a stability condition in the constant stepsize regime. Then, we introduce a novel decreasing stepsize version of ADAM. Under mild assumptions, it is shown that the iterates are almost surely bounded and converge almost surely to critical points of the objective function. Finally, we analyze the fluctuations of the algorithm by means of a conditional central limit theorem.In the second part of the thesis, in the vanishing stepsizes regime, we generalize our convergence and fluctuations results to a stochastic optimization procedure unifying several variants of the stochastic gradient descent such as, among others, the stochastic heavy ball method, the Stochastic Nesterov Accelerated Gradient algorithm, and the widely used ADAM algorithm. We conclude this second part by an avoidance of traps result establishing the non-convergence of the general algorithm to undesired critical points, such as local maxima or saddle points. Here, the main ingredient is a new avoidance of traps result for non-autonomous settings, which is of independent interest.Finally, the last part of this thesis which is independent from the two previous parts, is concerned with the analysis of a stochastic approximation algorithm for reinforcement learning. In this last part, we propose an analysis of an online target-based actor-critic algorithm with linear function approximation in the discounted reward setting. Our algorithm uses three different timescales: one for the actor and two for the critic. Instead of using the standard single timescale temporal difference (TD) learning algorithm as a critic, we use a two timescales target-based version of TD learning closely inspired from practical actor-critic algorithms implementing target networks. First, we establish asymptotic convergence results for both the critic and the actor under Markovian sampling. Then, we provide a finite-time analysis showing the impact of incorporating a target network into actor-critic methods
Vaudrey, Michael Allen. "Adaptive Control Methods for Non-Linear Self-Excited Systems". Diss., Virginia Tech, 2001. http://hdl.handle.net/10919/28884.
Pełny tekst źródłaPh. D.
O'Neal, Jerome W. "The use of preconditioned iterative linear solvers in interior-point methods and related topics". Diss., Available online, Georgia Institute of Technology, 2005, 2005. http://etd.gatech.edu/theses/available/etd-06242005-162854/.
Pełny tekst źródłaParker, R. Gary, Committee Member ; Shapiro, Alexander, Committee Member ; Nemirovski, Arkadi, Committee Member ; Green, William, Committee Member ; Monteiro, Renato, Committee Chair.
Santos, Rodríguez Cristian de. "Backanalysis methodology based on multiple optimization techniques for geotechnical problems". Doctoral thesis, Universitat Politècnica de Catalunya, 2015. http://hdl.handle.net/10803/334179.
Pełny tekst źródłaActualmente, gracias al aumento de la capacidad de los ordenadores para resolver problemas grandes y complejos, y gracias también al gran esfuerzo de la comunidad geotécnica de definir mejores y más sofisticados modelos constitutivos, se ha abordado el reto de predecir y simular el comportamiento del terreno. Sin embargo, debido al aumento de esa sofisticación, también ha aumentado el número de parámetros que definen el problema. Además, frecuentemente, muchos de esos parámetros no tienen un sentido geotécnico real dado que vienen directamente de expresiones puramente matemáticas, lo cual dificulta su identificación. Como consecuencia, es necesario un mayor esfuerzo en la identificación de los parámetros para poder definir apropiadamente el problema. Esta tesis pretende proporcionar una metodología que facilite la identificación mediante el análisis inverso de los parámetros de modelos constitutivos del terreno. Los mejores parámetros se definen como aquellos que minimizan una función objetivo basada en la diferencia entre medidas y valores calculados. Diferentes técnicas de optimización han sido utilizadas en este estudio, desde las más tradicionales, como los métodos basados en el gradiente, hasta las más modernas, como los algoritmos genéticos adaptativos y los métodos híbridos. De este estudio, se han extraído varias recomendaciones para sacar el mayor provecho de cada una de las técnicas de optimización. Además, se ha llevado a cabo un análisis extensivo para determinar la influencia sobre qué medir, dónde medir y cuándo medir en el contexto de la excavación de un túnel. El código de Elementos Finitos Plaxis ha sido utilizado como herramienta de cálculo del problema directo. El desarrollo de un código FORTRAN ha sido necesario para automatizar todo el procedimiento de Análisis Inverso. El modelo constitutivo de Hardening Soil ha sido adoptado para simular el comportamiento del terreno. Varios parámetros del modelo constitutivo de Hardening implementado en Plaxis, como E_50^ref, E_ur^ref, c y f, han sido identificados para diferentes escenarios geotécnicos. Primero, se ha utilizado un caso sintético de un túnel donde se han analizado todas las distintas técnicas que han sido propuestas en esta tesis. Después, dos casos reales complejos de una construcción de un túnel (Línea 9 del Metro de Barcelona) y una gran excavación (Estación de Girona del Tren de Alta Velocidad) se han presentado para ilustrar el potencial de la metodología. Un enfoque especial en la influencia del procedimiento constructivo y la estructura del error de las medidas se le ha dado al análisis inverso del túnel, mientras que en el análisis inverso de la estación el esfuerzo se ha centrado más en el concepto del diseño adaptativo mediante el análisis inverso. Además, otro caso real, algo menos convencional en términos geotécnicos, como es la exploración de la superficie de Marte mediante robots, ha sido presentado para examinar la metodología y la fiabilidad del modelo de interacción suelo-rueda de Wong y Reece; extensamente adoptado por la comunidad que trabajo en Terramecánica, pero aún no totalmente aceptada para robots ligeros como los que se han utilizado recientemente en las misiones de exploración de Marte.
Vu, Do Huy Cuong. "Méthodes numériques pour les écoulements et le transport en milieu poreux". Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112348/document.
Pełny tekst źródłaThis thesis bears on the modelling of groundwater flow and transport in porous media; we perform numerical simulations by means of finite volume methods and prove convergence results. In Chapter 1, we first apply a semi-implicit standard finite volume method and then the generalized finite volume method SUSHI for the numerical simulation of density driven flows in porous media; we solve a nonlinear convection-diffusion parabolic equation for the concentration coupled with an elliptic equation for the pressure. We apply the standard finite volume method to compute the solutions of a problem involving a rotating interface between salt and fresh water and of Henry's problem. We then apply the SUSHI scheme to the same problems as well as to a three dimensional saltpool problem. We use adaptive meshes, based upon square volume elements in space dimension two and cubic volume elements in space dimension three. In Chapter 2, we apply the generalized finite volume method SUSHI to the discretization of Richards equation, an elliptic-parabolic equation modeling groundwater flow, where the diffusion term can be anisotropic and heterogeneous. This class of locally conservative methods can be applied to a wide range of unstructured possibly non-matching polyhedral meshes in arbitrary space dimension. As is needed for Richards equation, the time discretization is fully implicit. We obtain a convergence result based upon a priori estimates and the application of the Fréchet-Kolmogorov compactness theorem. We implement the scheme and present numerical tests. In Chapter 3, we study a gradient scheme for the Signorini problem. Gradient schemes are nonconforming methods written in discrete variational formulation which are based on independent approximations of the functions and the gradients. We prove the existence and uniqueness of the discrete solution as well as its convergence to the weak solution of the Signorini problem. Finally we introduce a numerical scheme based upon the SUSHI discretization and present numerical results. In Chapter 4, we apply a semi-implicit scheme in time together with a generalized finite volume method for the numerical solution of density driven flows in porous media; it comes to solve nonlinear convection-diffusion parabolic equations for the solute and temperature transport as well as for the pressure. We compute the solutions for a specific problem which describes the advance of a warm fresh water front coupled to heat transfer in a confined aquifer which is initially charged with cold salt water. We use adaptive meshes, based upon square volume elements in space dimension two
"Adaptive Curvature for Stochastic Optimization". Master's thesis, 2019. http://hdl.handle.net/2286/R.I.53675.
Pełny tekst źródłaDissertation/Thesis
Masters Thesis Computer Science 2019
Książki na temat "Adaptive gradient methods with momentum"
King, John. Application of the conjugate gradient method to the COMMIX-1B three dimensional momentum equation. 1987.
Znajdź pełny tekst źródłaCzęści książek na temat "Adaptive gradient methods with momentum"
Alexander, S. Thomas. "Gradient Adaptive Lattice Methods". W Adaptive Signal Processing, 99–110. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4612-4978-8_7.
Pełny tekst źródłaTsoi, Ah Chung, i Ah Chung Tsoi. "Gradient based learning methods". W Adaptive Processing of Sequences and Data Structures, 27–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0053994.
Pełny tekst źródłaZhai, Tingting, Hao Wang, Frédéric Koriche i Yang Gao. "Online Feature Selection by Adaptive Sub-gradient Methods". W Machine Learning and Knowledge Discovery in Databases, 430–46. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10928-8_26.
Pełny tekst źródłaKonnov, Igor V. "Adaptive Step-Size for Non-monotone Gradient Type Methods". W Encyclopedia of Optimization, 1–4. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-54621-2_792-1.
Pełny tekst źródłaZhang, Xin, Can Cui, Wen-Jian Cai, Hui Cai i Gang Jing. "A Gradient-Based Online Adaptive Air Balancing Method". W Principle, Design and Optimization of Air Balancing Methods for the Multi-zone Ventilation Systems in Low Carbon Green Buildings, 35–53. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-7091-7_3.
Pełny tekst źródłaRehman, M. Z., i N. M. Nawi. "The Effect of Adaptive Momentum in Improving the Accuracy of Gradient Descent Back Propagation Algorithm on Classification Problems". W Software Engineering and Computer Systems, 380–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22170-5_33.
Pełny tekst źródłaAbbasi, Hamidreza, Ali Akbar Safavi i Maryam Salimifard. "Type-2 Fuzzy Wavelet Neural Network Controller Design Based on an Adaptive Gradient Descent Method for Nonlinear Dynamic Systems". W Applied Methods and Techniques for Mechatronic Systems, 229–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36385-6_12.
Pełny tekst źródłaHong, Dian, i Deng Chen. "Gradient-Based Adversarial Example Generation with Root Mean Square Propagation". W Artificial Intelligence and Human-Computer Interaction. IOS Press, 2024. http://dx.doi.org/10.3233/faia240141.
Pełny tekst źródła"Gradient Methods Using Momentum". W Optimization for Data Analysis, 55–74. Cambridge University Press, 2022. http://dx.doi.org/10.1017/9781009004282.005.
Pełny tekst źródła"Adaptive Gradient Methods". W Inference and Learning from Data, 599–641. Cambridge University Press, 2022. http://dx.doi.org/10.1017/9781009218146.018.
Pełny tekst źródłaStreszczenia konferencji na temat "Adaptive gradient methods with momentum"
Chen, Jinghui, Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao i Quanquan Gu. "Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks". W Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/452.
Pełny tekst źródłaZhou, Beitong, Jun Liu, Weigao Sun, Ruijuan Chen, Claire Tomlin i Ye Yuan. "pbSGD: Powered Stochastic Gradient Descent Methods for Accelerated Non-Convex Optimization". W Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/451.
Pełny tekst źródłaLi, Sumu, Dequan Li i Yuheng Zhang. "Incorporating Nesterov’s Momentum into Distributed Adaptive Gradient Method for Online Optimization". W 2021 China Automation Congress (CAC). IEEE, 2021. http://dx.doi.org/10.1109/cac53003.2021.9727247.
Pełny tekst źródłaWeng, Bowen, Huaqing Xiong, Yingbin Liang i Wei Zhang. "Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descent". W Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/422.
Pełny tekst źródłaWu, Jui-Yu, i Pei-Ci Liu. "Identifying a Default of Credit Card Clients by using a LSTM Method: A Case Study". W 8th International Conference on Artificial Intelligence (ARIN 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121012.
Pełny tekst źródłaXu, Chenyuan, Kosuke Haruki, Taiji Suzuki, Masahiro Ozawa, Kazuki Uematsu i Ryuji Sakai. "Data-Parallel Momentum Diagonal Empirical Fisher (DP-MDEF):Adaptive Gradient Method is Affected by Hessian Approximation and Multi-Class Data". W 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2022. http://dx.doi.org/10.1109/icmla55696.2022.00221.
Pełny tekst źródłaOlivett, A., P. Corrao i M. A. Karami. "Flow Control and Separation Delay in Morphing Wing Aircraft Using Traveling Wave Actuation". W ASME 2020 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/smasis2020-2355.
Pełny tekst źródłaMasood, Sarfaraz, M. N. Doja i Pravin Chandra. "Analysis of weight initialization methods for gradient descent with momentum". W 2015 International Conference on Soft Computing Techniques and Implementations (ICSCTI). IEEE, 2015. http://dx.doi.org/10.1109/icscti.2015.7489618.
Pełny tekst źródłaSun, Huikang, Lize Gu i Bin Sun. "Adathm: Adaptive Gradient Method Based on Estimates of Third-Order Moments". W 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC). IEEE, 2019. http://dx.doi.org/10.1109/dsc.2019.00061.
Pełny tekst źródłaRen, Xuchun, i Sharif Rahman. "Robust Design Optimization by Adaptive-Sparse Polynomial Dimensional Decomposition". W ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59691.
Pełny tekst źródłaRaporty organizacyjne na temat "Adaptive gradient methods with momentum"
Sett, Dominic, Florian Waldschmidt, Alvaro Rojas-Ferreira, Saut Sagala, Teresa Arce Mojica, Preeti Koirala, Patrick Sanady i in. Climate and disaster risk analytics tool for adaptive social protection. United Nations University - Institute for Environment and Human Security, marzec 2022. http://dx.doi.org/10.53324/wnsg2302.
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