Literatura académica sobre el tema "Stochastic gratient"
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Artículos de revistas sobre el tema "Stochastic gratient"
Li, Huiyan. "A Novel Machine Translation Method based on Stochastic Finite Automata Model for Spoken English". International Journal of Emerging Technologies in Learning (iJET) 14, n.º 06 (29 de marzo de 2019): 98. http://dx.doi.org/10.3991/ijet.v14i06.10161.
Texto completoSolomon, Joshua A. y Michael J. Morgan. "Stochastic re-calibration: contextual effects on perceived tilt". Proceedings of the Royal Society B: Biological Sciences 273, n.º 1601 (11 de julio de 2006): 2681–86. http://dx.doi.org/10.1098/rspb.2006.3634.
Texto completovon Martens, Hans-Jürgen. "Investigations into the Uncertainties of Interferometric Measurements of Linear and Circular Vibrations". Shock and Vibration 4, n.º 5-6 (1997): 327–40. http://dx.doi.org/10.1155/1997/183527.
Texto completoJuan, M. L., J. Plain, R. Bachelot, P. Royer, S. K. Gray y G. P. Wiederrecht. "Stochastic model for photoinduced surface relief grating formation through molecular transport in polymer films". Applied Physics Letters 93, n.º 15 (13 de octubre de 2008): 153304. http://dx.doi.org/10.1063/1.2999625.
Texto completoBai, Ping, Mohamed S. Abdelkhalik, Diogo G. A. Castanheira y Jaime Gómez Rivas. "Evolutionary optimization of the short-circuit current enhancement in organic solar cells by nanostructured electrodes". Journal of Applied Physics 132, n.º 15 (21 de octubre de 2022): 153103. http://dx.doi.org/10.1063/5.0097964.
Texto completoZhao, Long, Xinbo Huang, Jianyuan Jia, Yongcan Zhu y Wen Cao. "Detection of Broken Strands of Transmission Line Conductors Using Fiber Bragg Grating Sensors". Sensors 18, n.º 7 (23 de julio de 2018): 2397. http://dx.doi.org/10.3390/s18072397.
Texto completoSHINOHARA, N., B. SHISHKOV, H. MATSUMOTO, K. HASHIMOTO y A. K. M. BAKI. "New Stochastic Algorithm for Optimization of Both Side Lobes and Grating Lobes in Large Antenna Arrays for MPT". IEICE Transactions on Communications E91-B, n.º 1 (1 de enero de 2008): 286–96. http://dx.doi.org/10.1093/ietcom/e91-b.1.286.
Texto completoWu, G. y Y. Cai. "Polarization ellipse and Stokes parameters of a stochastic electromagnetic Gaussian Schell-model beam propagating through a polarization grating". Applied Physics B 105, n.º 4 (8 de julio de 2011): 893–907. http://dx.doi.org/10.1007/s00340-011-4607-z.
Texto completoLi, Sheng, Liang Jin, Jinpeng Jiang, Honghai Wang, Qiuming Nan y Lizhi Sun. "Looseness Identification of Track Fasteners Based on Ultra-Weak FBG Sensing Technology and Convolutional Autoencoder Network". Sensors 22, n.º 15 (28 de julio de 2022): 5653. http://dx.doi.org/10.3390/s22155653.
Texto completoPlock, Matthias, Martin Hammerschmidt, Sven Burger, Philipp-Immanuel Schneider y Christof Schutte. "Impact study of numerical discretization accuracy on parameter reconstructions and model parameter distributions". Metrologia, 6 de julio de 2023. http://dx.doi.org/10.1088/1681-7575/ace4cd.
Texto completoTesis sobre el tema "Stochastic gratient"
Koroko, Abdoulaye. "Natural gradient-based optimization methods for deep neural networks". Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG068.
Texto completoThe stochastic gradient method is currently the prevailing technology for training neural networks. Compared to a classical descent, the calculation of the true gradient as an average over the data is replaced by a random element of the sum. When dealing with massive data, this bold approximation enables one to decrease the number of elementary gradient evaluations and to alleviate the cost of each iteration. The price to be paid is the appearance of oscillations and the slowness of convergence, which is often excessive in terms of number of iterations. The aim of this thesis is to design an approach that is both: (i) more robust, using the fundamental methods that have been successfully proven in classical optimization, i.e., outside the learning framework; and (ii) faster in terms of convergence speed. We are especially interested in second-order methods, known for their stability and speed of convergence. To circumvent the bottleneck of these methods, which lies in the prohibitive cost of an iteration involving a linear system with a full matrix, we attempt to improve an approximation recently introduced as Kronecker-Factorized Approximation of Curvature (KFAC) for the Fisher matrix, which replaces the Hessian matrix in this context. More specifically, our work focuses on: (i) building new Kronecker factorizations based on a more rigorous mathematical justification than in KFAC; (ii) taking into account the information from the off-diagonal blocks of the Fisher matrix, which represent the interaction between the different layers; (iii) generalizing KFAC to a network architecture other than those for which it had been initially developed
Actas de conferencias sobre el tema "Stochastic gratient"
Glabisch, Sven, Sophia Schröder, Sascha Brose, Henning Heiming, Jochen Stollenwerk y Carlo Holly. "Investigation of stochastic roughness effects for nanoscale grating characterization with a stand-alone EUV spectrometer". En Photomask Technology 2022, editado por Bryan S. Kasprowicz y Ted Liang. SPIE, 2022. http://dx.doi.org/10.1117/12.2641625.
Texto completoMukamel, Shaul, Zhifang Deng y Roger F. Loring. "Time-Domain And Frequency-Domain Four-Wave Mixing; A Unified Stochastic Approach". En International Conference on Ultrafast Phenomena. Washington, D.C.: Optica Publishing Group, 1986. http://dx.doi.org/10.1364/up.1986.thc7.
Texto completoMait, Joseph N. "Complex plane representation and design of array generators". En OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.wa3.
Texto completoFourkas, John T., Rick Trebino, M. D. Fayer y Mark A. Dugan. "Extra Resonances in Time-Domain Nonlinear Spectroscopies". En Nonlinear Optics. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/nlo.1992.tud2.
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