Gotowa bibliografia na temat „Stochastic gratient”
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Artykuły w czasopismach na temat "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, nr 06 (29.03.2019): 98. http://dx.doi.org/10.3991/ijet.v14i06.10161.
Pełny tekst źródłaSolomon, Joshua A., i Michael J. Morgan. "Stochastic re-calibration: contextual effects on perceived tilt". Proceedings of the Royal Society B: Biological Sciences 273, nr 1601 (11.07.2006): 2681–86. http://dx.doi.org/10.1098/rspb.2006.3634.
Pełny tekst źródłavon Martens, Hans-Jürgen. "Investigations into the Uncertainties of Interferometric Measurements of Linear and Circular Vibrations". Shock and Vibration 4, nr 5-6 (1997): 327–40. http://dx.doi.org/10.1155/1997/183527.
Pełny tekst źródłaJuan, M. L., J. Plain, R. Bachelot, P. Royer, S. K. Gray i G. P. Wiederrecht. "Stochastic model for photoinduced surface relief grating formation through molecular transport in polymer films". Applied Physics Letters 93, nr 15 (13.10.2008): 153304. http://dx.doi.org/10.1063/1.2999625.
Pełny tekst źródłaBai, Ping, Mohamed S. Abdelkhalik, Diogo G. A. Castanheira i Jaime Gómez Rivas. "Evolutionary optimization of the short-circuit current enhancement in organic solar cells by nanostructured electrodes". Journal of Applied Physics 132, nr 15 (21.10.2022): 153103. http://dx.doi.org/10.1063/5.0097964.
Pełny tekst źródłaZhao, Long, Xinbo Huang, Jianyuan Jia, Yongcan Zhu i Wen Cao. "Detection of Broken Strands of Transmission Line Conductors Using Fiber Bragg Grating Sensors". Sensors 18, nr 7 (23.07.2018): 2397. http://dx.doi.org/10.3390/s18072397.
Pełny tekst źródłaSHINOHARA, N., B. SHISHKOV, H. MATSUMOTO, K. HASHIMOTO i 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, nr 1 (1.01.2008): 286–96. http://dx.doi.org/10.1093/ietcom/e91-b.1.286.
Pełny tekst źródłaWu, G., i Y. Cai. "Polarization ellipse and Stokes parameters of a stochastic electromagnetic Gaussian Schell-model beam propagating through a polarization grating". Applied Physics B 105, nr 4 (8.07.2011): 893–907. http://dx.doi.org/10.1007/s00340-011-4607-z.
Pełny tekst źródłaLi, Sheng, Liang Jin, Jinpeng Jiang, Honghai Wang, Qiuming Nan i Lizhi Sun. "Looseness Identification of Track Fasteners Based on Ultra-Weak FBG Sensing Technology and Convolutional Autoencoder Network". Sensors 22, nr 15 (28.07.2022): 5653. http://dx.doi.org/10.3390/s22155653.
Pełny tekst źródłaPlock, Matthias, Martin Hammerschmidt, Sven Burger, Philipp-Immanuel Schneider i Christof Schutte. "Impact study of numerical discretization accuracy on parameter reconstructions and model parameter distributions". Metrologia, 6.07.2023. http://dx.doi.org/10.1088/1681-7575/ace4cd.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaThe 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
Streszczenia konferencji na temat "Stochastic gratient"
Glabisch, Sven, Sophia Schröder, Sascha Brose, Henning Heiming, Jochen Stollenwerk i Carlo Holly. "Investigation of stochastic roughness effects for nanoscale grating characterization with a stand-alone EUV spectrometer". W Photomask Technology 2022, redaktorzy Bryan S. Kasprowicz i Ted Liang. SPIE, 2022. http://dx.doi.org/10.1117/12.2641625.
Pełny tekst źródłaMukamel, Shaul, Zhifang Deng i Roger F. Loring. "Time-Domain And Frequency-Domain Four-Wave Mixing; A Unified Stochastic Approach". W International Conference on Ultrafast Phenomena. Washington, D.C.: Optica Publishing Group, 1986. http://dx.doi.org/10.1364/up.1986.thc7.
Pełny tekst źródłaMait, Joseph N. "Complex plane representation and design of array generators". W OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.wa3.
Pełny tekst źródłaFourkas, John T., Rick Trebino, M. D. Fayer i Mark A. Dugan. "Extra Resonances in Time-Domain Nonlinear Spectroscopies". W Nonlinear Optics. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/nlo.1992.tud2.
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