Academic literature on the topic 'Stochastic gratient'
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Journal articles on the topic "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, no. 06 (March 29, 2019): 98. http://dx.doi.org/10.3991/ijet.v14i06.10161.
Full textSolomon, Joshua A., and Michael J. Morgan. "Stochastic re-calibration: contextual effects on perceived tilt." Proceedings of the Royal Society B: Biological Sciences 273, no. 1601 (July 11, 2006): 2681–86. http://dx.doi.org/10.1098/rspb.2006.3634.
Full textvon Martens, Hans-Jürgen. "Investigations into the Uncertainties of Interferometric Measurements of Linear and Circular Vibrations." Shock and Vibration 4, no. 5-6 (1997): 327–40. http://dx.doi.org/10.1155/1997/183527.
Full textJuan, M. L., J. Plain, R. Bachelot, P. Royer, S. K. Gray, and G. P. Wiederrecht. "Stochastic model for photoinduced surface relief grating formation through molecular transport in polymer films." Applied Physics Letters 93, no. 15 (October 13, 2008): 153304. http://dx.doi.org/10.1063/1.2999625.
Full textBai, Ping, Mohamed S. Abdelkhalik, Diogo G. A. Castanheira, and Jaime Gómez Rivas. "Evolutionary optimization of the short-circuit current enhancement in organic solar cells by nanostructured electrodes." Journal of Applied Physics 132, no. 15 (October 21, 2022): 153103. http://dx.doi.org/10.1063/5.0097964.
Full textZhao, Long, Xinbo Huang, Jianyuan Jia, Yongcan Zhu, and Wen Cao. "Detection of Broken Strands of Transmission Line Conductors Using Fiber Bragg Grating Sensors." Sensors 18, no. 7 (July 23, 2018): 2397. http://dx.doi.org/10.3390/s18072397.
Full textSHINOHARA, N., B. SHISHKOV, H. MATSUMOTO, K. HASHIMOTO, and 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, no. 1 (January 1, 2008): 286–96. http://dx.doi.org/10.1093/ietcom/e91-b.1.286.
Full textWu, G., and Y. Cai. "Polarization ellipse and Stokes parameters of a stochastic electromagnetic Gaussian Schell-model beam propagating through a polarization grating." Applied Physics B 105, no. 4 (July 8, 2011): 893–907. http://dx.doi.org/10.1007/s00340-011-4607-z.
Full textLi, Sheng, Liang Jin, Jinpeng Jiang, Honghai Wang, Qiuming Nan, and Lizhi Sun. "Looseness Identification of Track Fasteners Based on Ultra-Weak FBG Sensing Technology and Convolutional Autoencoder Network." Sensors 22, no. 15 (July 28, 2022): 5653. http://dx.doi.org/10.3390/s22155653.
Full textPlock, Matthias, Martin Hammerschmidt, Sven Burger, Philipp-Immanuel Schneider, and Christof Schutte. "Impact study of numerical discretization accuracy on parameter reconstructions and model parameter distributions." Metrologia, July 6, 2023. http://dx.doi.org/10.1088/1681-7575/ace4cd.
Full textDissertations / Theses on the topic "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.
Full textThe 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
Conference papers on the topic "Stochastic gratient"
Glabisch, Sven, Sophia Schröder, Sascha Brose, Henning Heiming, Jochen Stollenwerk, and Carlo Holly. "Investigation of stochastic roughness effects for nanoscale grating characterization with a stand-alone EUV spectrometer." In Photomask Technology 2022, edited by Bryan S. Kasprowicz and Ted Liang. SPIE, 2022. http://dx.doi.org/10.1117/12.2641625.
Full textMukamel, Shaul, Zhifang Deng, and Roger F. Loring. "Time-Domain And Frequency-Domain Four-Wave Mixing; A Unified Stochastic Approach." In International Conference on Ultrafast Phenomena. Washington, D.C.: Optica Publishing Group, 1986. http://dx.doi.org/10.1364/up.1986.thc7.
Full textMait, Joseph N. "Complex plane representation and design of array generators." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.wa3.
Full textFourkas, John T., Rick Trebino, M. D. Fayer, and Mark A. Dugan. "Extra Resonances in Time-Domain Nonlinear Spectroscopies." In Nonlinear Optics. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/nlo.1992.tud2.
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