Relevant bibliographies by topics / Stochastic weights

Academic literature on the topic 'Stochastic weights'

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Published: 6 September 2023

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Journal articles on the topic "Stochastic weights"

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Xiong, Fenfen, Wei Chen, Ying Xiong, and Shuxing Yang. "Weighted stochastic response surface method considering sample weights." Structural and Multidisciplinary Optimization 43, no. 6 (February 3, 2011): 837–49. http://dx.doi.org/10.1007/s00158-011-0621-3.

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Li, Yan, and Yi Shen. "Preserving Global Exponential Stability of Hybrid BAM Neural Networks with Reaction Diffusion Terms in the Presence of Stochastic Noise and Connection Weight Matrices Uncertainty." Mathematical Problems in Engineering 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/486052.

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We study the impact of stochastic noise and connection weight matrices uncertainty on global exponential stability of hybrid BAM neural networks with reaction diffusion terms. Given globally exponentially stable hybrid BAM neural networks with reaction diffusion terms, the question to be addressed here is how much stochastic noise and connection weights matrices uncertainty the neural networks can tolerate while maintaining global exponential stability. The upper threshold of stochastic noise and connection weights matrices uncertainty is defined by using the transcendental equations. We find that the perturbed hybrid BAM neural networks with reaction diffusion terms preserve global exponential stability if the intensity of both stochastic noise and connection weights matrices uncertainty is smaller than the defined upper threshold. A numerical example is also provided to illustrate the theoretical conclusion.
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Goldenberg, David H. "Beta Instability and Stochastic Market Weights." Management Science 31, no. 4 (April 1985): 415–21. http://dx.doi.org/10.1287/mnsc.31.4.415.

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Caraballo, Luis E., Pablo Pérez-Lantero, Carlos Seara, and Inmaculada Ventura. "Maximum Box Problem on Stochastic Points." Algorithmica 83, no. 12 (October 28, 2021): 3741–65. http://dx.doi.org/10.1007/s00453-021-00882-z.

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AbstractGiven a finite set of weighted points in $${\mathbb {R}}^d$$ R d (where there can be negative weights), the maximum box problem asks for an axis-aligned rectangle (i.e., box) such that the sum of the weights of the points that it contains is maximized. We consider that each point of the input has a probability of being present in the final random point set, and these events are mutually independent; then, the total weight of a maximum box is a random variable. We aim to compute both the probability that this variable is at least a given parameter, and its expectation. We show that even in $$d=1$$ d = 1 these computations are #P-hard, and give pseudo-polynomial time algorithms in the case where the weights are integers in a bounded interval. For $$d=2$$ d = 2 , we consider that each point is colored red or blue, where red points have weight $$+1$$ + 1 and blue points weight $$-\infty $$ - ∞ . The random variable is the maximum number of red points that can be covered with a box not containing any blue point. We prove that the above two computations are also #P-hard, and give a polynomial-time algorithm for computing the probability that there is a box containing exactly two red points, no blue point, and a given point of the plane.
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Yang, Zao-li, and Lu-cheng Huang. "Dynamic Stochastic Multiattribute Decision-Making That Considers Stochastic Variable Variance Characteristics under Time-Sequence Contingency Environments." Mathematical Problems in Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/7126856.

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This paper presents a dynamic stochastic decision-making method that considers the characteristics of stochastic variable variances under time-sequence contingency environments for solving stochastic decision-making problems with information from different periods and of indeterminate attribute weights. First, time-sequence weights are obtained using the technique for order preference by similarity to ideal solution (TOPSIS), corresponding with the idea of “stressing the present rather than the past.” After determining the time degree and fully considering the characteristics of normally distributed stochastic variable variances, the attribute weight is determined based on vertical projection distance. Decision-making information is then assembled from two dimensions of time-sequence and attributes, based on the two categories of weighted arithmetic averaging operators of normally distributed stochastic variables, resulting in comprehensive dynamic decision-making from single solution dimensions and a priority sequence of solutions per the order relation criteria of normally distributed stochastic variables. Finally, the validity and practicability of the methods proposed in this paper are verified using an example numerical analysis.
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Gashi, Bujar. "Optimal stochastic regulators with state-dependent weights." Systems & Control Letters 134 (December 2019): 104522. http://dx.doi.org/10.1016/j.sysconle.2019.104522.

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Xu, Liyan, Tony Vladusich, Fabing Duan, Lachlan J. Gunn, Derek Abbott, and Mark D. McDonnell. "Decoding suprathreshold stochastic resonance with optimal weights." Physics Letters A 379, no. 38 (October 2015): 2277–83. http://dx.doi.org/10.1016/j.physleta.2015.05.032.

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Brüggemann, Ralf, and Helmut Lütkepohl. "Forecasting contemporaneous aggregates with stochastic aggregation weights." International Journal of Forecasting 29, no. 1 (January 2013): 60–68. http://dx.doi.org/10.1016/j.ijforecast.2012.05.007.

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Liu, Qingliang, and Jinmei Lai. "Stochastic Loss Function." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4884–91. http://dx.doi.org/10.1609/aaai.v34i04.5925.

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Training deep neural networks is inherently subject to the predefined and fixed loss functions during optimizing. To improve learning efficiency, we develop Stochastic Loss Function (SLF) to dynamically and automatically generating appropriate gradients to train deep networks in the same round of back-propagation, while maintaining the completeness and differentiability of the training pipeline. In SLF, a generic loss function is formulated as a joint optimization problem of network weights and loss parameters. In order to guarantee the requisite efficiency, gradients with the respect to the generic differentiable loss are leveraged for selecting loss function and optimizing network weights. Extensive experiments on a variety of popular datasets strongly demonstrate that SLF is capable of obtaining appropriate gradients at different stages during training, and can significantly improve the performance of various deep models on real world tasks including classification, clustering, regression, neural machine translation, and objection detection.
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Guo, Hao, Jiyong Jin, and Bin Liu. "Stochastic Weight Averaging Revisited." Applied Sciences 13, no. 5 (February 24, 2023): 2935. http://dx.doi.org/10.3390/app13052935.

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Averaging neural network weights sampled by a backbone stochastic gradient descent (SGD) is a simple-yet-effective approach to assist the backbone SGD in finding better optima, in terms of generalization. From a statistical perspective, weight-averaging contributes to variance reduction. Recently, a well-established stochastic weight-averaging (SWA) method was proposed, which featured the application of a cyclical or high-constant (CHC) learning-rate schedule for generating weight samples for weight-averaging. Then, a new insight on weight-averaging was introduced, which stated that weight average assisted in discovering a wider optima and resulted in better generalization. We conducted extensive experimental studies concerning SWA, involving 12 modern deep neural network model architectures and 12 open-source image, graph, and text datasets as benchmarks. We disentangled the contributions of the weight-averaging operation and the CHC learning-rate schedule for SWA, showing that the weight-averaging operation in SWA still contributed to variance reduction, and the CHC learning-rate schedule assisted in exploring the parameter space more widely than the backbone SGD, which could be be under-fitted due to a lack of training budget. We then presented an algorithm termed periodic SWA (PSWA) that comprised a series of weight-averaging operations to exploit such wide parameter space structures as explored by the CHC learning-rate schedule, and we empirically demonstrated that PSWA outperformed its backbone SGD remarkably.
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Dissertations / Theses on the topic "Stochastic weights"

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Dohndorf, Iryna [Verfasser], Peter [Akademischer Betreuer] Buchholz, and Boudewijn R. [Gutachter] Haverkort. "Stochastic graph models with phase type distributed edge weights / Iryna Dohndorf ; Gutachter: Boudewijn R. Haverkort ; Betreuer: Peter Buchholz." Dortmund : Universitätsbibliothek Dortmund, 2017. http://d-nb.info/1134953046/34.

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Medeiros, Júnior Maurício da Silva. "Stochastic discount factor bounds and rare events: a review." reponame:Repositório Institucional do FGV, 2016. http://hdl.handle.net/10438/16459.

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We aim to provide a review of the stochastic discount factor bounds usually applied to diagnose asset pricing models. In particular, we mainly discuss the bounds used to analyze the disaster model of Barro (2006). Our attention is focused in this disaster model since the stochastic discount factor bounds that are applied to study the performance of disaster models usually consider the approach of Barro (2006). We first present the entropy bounds that provide a diagnosis of the analyzed disaster model which are the methods of Almeida and Garcia (2012, 2016); Ghosh et al. (2016). Then, we discuss how their results according to the disaster model are related to each other and also present the findings of other methodologies that are similar to these bounds but provide different evidence about the performance of the framework developed by Barro (2006).
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Silva, Emanuel Araújo. "MODELAGEM DINÂMICA PARA SIMULAÇÃO NO PROCESSO DE ARENIZAÇÃO E COBERTURA FLORESTAL NA CAMPANHA OCIDENTAL - RS." Universidade Federal de Santa Maria, 2015. http://repositorio.ufsm.br/handle/1/3781.

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The dynamic modeling process is a useful tool for the knowledge of land use and occupation, creating methodological guidelines associated to ambient, social and economical issues. This work aims to establish a model to simulate the dynamic in the sandfication process and forest cover at South-west of Rio Grande do Sul, named micro regions of Campanha Ocidental and, based on this technics, make a future scenery projection. An image mosaic of LANDSAT 5 satellite was used, which recovers the studied region in the years of 1985, 1996, 2011 and LANDSAT 8 in 2013 year. SPRING was used to data base elaboration and data processing of digital images. After the image classification, the LEGAL program was used to develop the cross thematic maps, which will be used on simulations for the future sceneries by modeling with Dinamica EGO software. The expected results for 2026 indicate that forest cover will increase from 14.22% in 2011 to 15,03% in the year 2026 the total area of the Campanha Ocidental, showing that the expansion of forest cover is in the process of stabilization, focusing the areas in east, high altitudes and around drainage rivers. In the sand, this projection will retracts from 0.37% in 2011 to 0.33% in 2026, its concentration will be in the northeast, high altitudes and around the Ibicuí river drainage.
A modelagem dinâmica é uma ferramenta útil para o conhecimento do uso e ocupação da terra, gerando diretrizes metodológicas associadas às questões ambientais, sociais e econômicas. Este trabalho teve por objetivo aplicar um modelo para simular a dinâmica no processo de arenização e cobertura florestal do Sudoeste do Rio Grande do Sul, denominada microrregião da Campanha Ocidental e, com base nessas técnicas, efetuar a projeção de cenários futuros. Foi utilizado um mosaico de imagens do satélite LANDSAT 5 sensor TM, que recobre a região de estudo nos anos de 1985, 1996 e 2011 e LANDSAT 8 sensor OLI no ano de 2013. Para elaboração da base de dados e processamento digital das imagens, utilizou-se o aplicativo SPRING. Após a classificação das imagens, foi realizado o cruzamento dos mapas temáticos com auxílio da programação LEGAL, e posteriormente, empregado a simulação dos cenários futuros por meio da modelagem com o aplicativo Dinamica EGO. Os resultados previstos para 2026 indicam que a cobertura florestal irá se expandir de 14,22% em 2011 para 15,03% no ano de 2026 da área total da Campanha Ocidental, demonstrando que o aumento da cobertura florestal encontra-se em processo de estabilização, concentrando-se suas áreas na parte leste, altitudes elevadas e nas bordas da rede de drenagem. Nos areais, a projeção demonstrou que sua área sofrerá retração de 0,37% em 2011 para 0,33% da área total da região em 2026, e sua concentração estará presente na parte leste, em altitudes elevadas e em torno da drenagem do rio Ibicui.
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Menz, William Jefferson. "Stochastic modelling of silicon nanoparticle synthesis." Thesis, University of Cambridge, 2014. https://www.repository.cam.ac.uk/handle/1810/245146.

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This thesis presents new methods to study the aerosol synthesis of nano-particles and a new model to simulate the formation of silicon nanoparticles. Population balance modelling is used to model nanoparticle synthesis and a stochastic numerical method is used to solve the governing equations. The population balance models are coupled to chemical kinetic models and offer insight into the fundamental physiochemical processes leading to particle formation. The first method developed in this work is a new mathematical expression for calculating the rate of Brownian coagulation with stochastic weighted algorithms (SWAs). The new expression permits the solution of the population balance equations with SWAs using a computationally-efficient technique of majorant rates and fictitious jumps. Convergence properties and efficiency of the expression are evaluated using a detailed silica particle model. A sequential-modular algorithm is subsequently presented which solves networks of perfectly stirred reactors with a population balance model using the stochastic method. The algorithm is tested in some simple network configurations, which are used to identify methods through which error in the stochastic solution may be reduced. It is observed that SWAs are useful in preventing accumulation of error in reactor networks. A new model for silicon nanoparticle synthesis is developed. The model includes gas-phase reactions describing silane decomposition, and a detailed multivariate particle model which tracks particle structure and composition. Systematic parameter estimation is used to fit the model to experimental cases. Results indicated that the key challenge in modelling silicon systems is obtaining a correct description of the particle nucleation process. Finally, the silicon model is used in conjunction with the reactor network algorithm to simulate the start-up of a plug-flow reactor. The power of stochastic methods in resolving characteristics of a particle ensemble is highlighted by investigating the number, size, degree of sintering and polydispersity along the length of the reactor.
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Kindl, Mark Richard. "A stochastic approach to path planning in the Weighted-Region Problem." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/26789.

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Rattana, Prapanporn. "Mean-field-like approximations for stochastic processes on weighted and dynamic networks." Thesis, University of Sussex, 2015. http://sro.sussex.ac.uk/id/eprint/56600/.

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The explicit use of networks in modelling stochastic processes such as epidemic dynamics has revolutionised research into understanding the impact of contact pattern properties, such as degree heterogeneity, preferential mixing, clustering, weighted and dynamic linkages, on how epidemics invade, spread and how to best control them. In this thesis, I worked on mean-field approximations of stochastic processes on networks with particular focus on weighted and dynamic networks. I mostly used low dimensional ordinary differential equation (ODE) models and explicit network-based stochastic simulations to model and analyse how epidemics become established and spread in weighted and dynamic networks. I begin with a paper presenting the susceptible-infected-susceptible/recovered (SIS, SIR) epidemic models on static weighted networks with different link weight distributions. This work extends the pairwise model paradigm to weighted networks and gives excellent agreement with simulations. The basic reproductive ratio, R0, is formulated for SIR dynamics. The effects of link weight distribution on R0 and on the spread of the disease are investigated in detail. This work is followed by a second paper, which considers weighted networks in which the nodal degree and weights are not independent. Moreover, two approximate models are explored: (i) the pairwise model and (ii) the edge-based compartmental model. These are used to derive important epidemic descriptors, including early growth rate, final epidemic size, basic reproductive ratio and epidemic dynamics. Whilst the first two papers concentrate on static networks, the third paper focuses on dynamic networks, where links can be activated and/or deleted and this process can evolve together with the epidemic dynamics. We consider an adaptive network with a link rewiring process constrained by spatial proximity. This model couples SIS dynamics with that of the network and it investigates the impact of rewiring on the network structure and disease die-out induced by the rewiring process. The fourth paper shows that the generalised master equations approach works well for networks with low degree heterogeneity but it fails to capture networks with modest or high degree heterogeneity. In particular, we show that a recently proposed generalisation performs poorly, except for networks with low heterogeneity and high average degree.
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Hilton, Cary Allen. "A stochastic approach to solving the 2 _x001B_p1_x001B_s/_x001B_b2_x001B_s dimensional weighted region problem." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/28563.

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This thesis describes a method of computing a feasible path solution for the anisotropic weighted region problem. Heuristics are used to locate an initial starting solution. This starting solution is iteratively improved using a golden ratio search to produce a solution within a specified tolerance. The path solution is then randomly perturbed or detoured through different region frontiers, and the golden ratio search is again applied. These random detours are controlled by a process known as simulated annealing, which determines the number of detours made and decides whether to accept or reject each path solution. Better solutions are always accepted and worse solutions are accepted based on a probability distribution. Accepting worse solutions allows an opportunity to escape from a local minimum condition and continue the search for the optimal path. Since an exhaustive search is not performed, the globally optimal path may not be found, but a feasible path can be found with this method
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Xu, Zhouyi. "Stochastic Modeling and Simulation of Gene Networks." Scholarly Repository, 2010. http://scholarlyrepository.miami.edu/oa_dissertations/645.

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Recent research in experimental and computational biology has revealed the necessity of using stochastic modeling and simulation to investigate the functionality and dynamics of gene networks. However, there is no sophisticated stochastic modeling techniques and efficient stochastic simulation algorithms (SSA) for analyzing and simulating gene networks. Therefore, the objective of this research is to design highly efficient and accurate SSAs, to develop stochastic models for certain real gene networks and to apply stochastic simulation to investigate such gene networks. To achieve this objective, we developed several novel efficient and accurate SSAs. We also proposed two stochastic models for the circadian system of Drosophila and simulated the dynamics of the system. The K-leap method constrains the total number of reactions in one leap to a properly chosen number thereby improving simulation accuracy. Since the exact SSA is a special case of the K-leap method when K=1, the K-leap method can naturally change from the exact SSA to an approximate leap method during simulation if necessary. The hybrid tau/K-leap and the modified K-leap methods are particularly suitable for simulating gene networks where certain reactant molecular species have a small number of molecules. Although the existing tau-leap methods can significantly speed up stochastic simulation of certain gene networks, the mean of the number of firings of each reaction channel is not equal to the true mean. Therefore, all existing tau-leap methods produce biased results, which limit simulation accuracy and speed. Our unbiased tau-leap methods remove the bias in simulation results that exist in all current leap SSAs and therefore significantly improve simulation accuracy without sacrificing speed. In order to efficiently estimate the probability of rare events in gene networks, we applied the importance sampling technique to the next reaction method (NRM) of the SSA and developed a weighted NRM (wNRM). We further developed a systematic method for selecting the values of importance sampling parameters. Applying our parameter selection method to the wSSA and the wNRM, we get an improved wSSA (iwSSA) and an improved wNRM (iwNRM), which can provide substantial improvement over the wSSA in terms of simulation efficiency and accuracy. We also develop a detailed and a reduced stochastic model for circadian rhythm in Drosophila and employ our SSA to simulate circadian oscillations. Our simulations showed that both models could produce sustained oscillations and that the oscillation is robust to noise in the sense that there is very little variability in oscillation period although there are significant random fluctuations in oscillation peeks. Moreover, although average time delays are essential to simulation of oscillation, random changes in time delays within certain range around fixed average time delay cause little variability in the oscillation period. Our simulation results also showed that both models are robust to parameter variations and that oscillation can be entrained by light/dark circles.
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Michel, Simon [Verfasser]. "Stochastic evolution equations in weighted L² spaces with jump noise / Simon Michel. Fakultät für Mathematik." Bielefeld : Universitätsbibliothek Bielefeld, Hochschulschriften, 2012. http://d-nb.info/1022030078/34.

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Szyszkowicz, B. (Barbara) Carleton University Dissertation Mathematics. "Weak convergence of stochastic processes in weighted metrics and their applications to contiguous changepoint analysis." Ottawa, 1992.

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Books on the topic "Stochastic weights"

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Dohndorf, Iryna. Stochastic graph models with phase type distributed edge weights. Dortmund: Universitätsbibliothek Dortmund, 2017.

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Lajos, Horváth, ed. Weighted approximations in probability and statistics. Chichester: Wiley, 1993.

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Kindl, Mark Richard. A stochastic approach to path planning in the Weighted-Region Problem. Monterey, Calif: Naval Postgraduate School, 1991.

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Hilton, Cary Allen. A stochastic approach to solving the 2 ¹/ dimensional weighted region problem. Monterey, Calif: Naval Postgraduate School, 1991.

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Kindl, Mark R. A stochastic approach to the weighted-region problem: 1. the design of the path annealing algorithm. Monterey, Calif: Naval Postgraduate School, 1991.

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P, McCormick William, ed. Asymptotic expansions for infinite weighted convolutions of heavy tail distributions and applications. Providence, R.I: American Mathematical Society, 2009.

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Alexander, Meskhi, and Persson Lars Erik 1944-, eds. Weighted norm inequalities for integral transforms with product kernals. Hauppauge, NY: Nova Science Publishers, 2009.

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Coolen, A. C. C., A. Annibale, and E. S. Roberts. Random graph ensembles. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0003.

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This chapter presents some theoretical tools for defining random graph ensembles systematically via soft or hard topological constraints including working through some properties of the Erdös-Rényi random graph ensemble, which is the simplest non-trivial random graph ensemble where links appear between two nodes with a fixed probability p. The chapter sets out the central representation of graph generation as the result of a discrete-time Markovian stochastic process. This unites the two flavours of graph generation approaches – because they can be viewed as simply moving forwards or backwards through this representation. It is possible to define a random graph by an algorithm, and then calculate the associated stationary probability. The alternative approach is to specify sampling weights and then to construct an algorithm that will have these weights as the stationary probabilities upon convergence.
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L, Taylor Robert. Stochastic Convergence of Weighted Sums of Random Elements in Linear Spaces. Springer London, Limited, 2006.

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Back, Kerry E. Alternative Preferences. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0025.

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The Allais and Ellsberg paradoxes are presented. Various generalizations of expected utility motivated by these and other paradoxes are discussed, including betweenness preferences, rank‐dependent preferences, multiple prior max‐min preferences, and prospect theory. For betweenness preferences, which include weighted utility and disappointment aversion, an investor’s marginal utility is proportional to a stochastic discount factor. Disappointment averse utility and rank‐dependent utility have first‐order risk aversion. Multiple prior max‐min utility is one way to accomodate the Ellsberg paradox (ambiguity aversion or Knightian uncertainty). The dynamic consistency of updating multiple priors is discussed.
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Book chapters on the topic "Stochastic weights"

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Rigatos, Gerasimos G. "Attractors in Associative Memories with Stochastic Weights." In Advanced Models of Neural Networks, 191–206. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43764-3_10.

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Rigatos, Gerasimos G. "Spectral Analysis of Neural Models with Stochastic Weights." In Advanced Models of Neural Networks, 207–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43764-3_11.

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Wu, Ying, Colin Fyfe, and Pei Ling Lai. "Stochastic Weights Reinforcement Learning for Exploratory Data Analysis." In Lecture Notes in Computer Science, 668–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74690-4_68.

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Birnbaum, Michael H. "The Paradoxes of Allais, Stochastic Dominance, and Decision Weights." In Decision Science and Technology, 27–52. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5089-1_3.

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Scholz, Roland W. "The ‘Base-Rate Fallacy’ — Heuristics and/or the Modeling of Judgmental Biases by Information Weights." In Cognitive Strategies in Stochastic Thinking, 10–56. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3825-0_2.

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Lee, Haesung, Wilhelm Stannat, and Gerald Trutnau. "The Abstract Cauchy Problem in L r-Spaces with Weights." In Analytic Theory of Itô-Stochastic Differential Equations with Non-smooth Coefficients, 9–57. Singapore: Springer Nature Singapore, 2012. http://dx.doi.org/10.1007/978-981-19-3831-3_2.

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Krylov, Nicolai V. "On Parabolic Pdes and Spdes in Sobolev Spaces W P 2 without and with Weights." In Topics in Stochastic Analysis and Nonparametric Estimation, 151–97. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-75111-5_8.

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Broadie, Mark, Paul Glasserman, and Zachary Ha. "Pricing American Options by Simulation Using a Stochastic Mesh with Optimized Weights." In Nonconvex Optimization and Its Applications, 26–44. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3150-7_2.

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Biehl, Michael. "The Statistical Physics of Learning Revisited: Typical Learning Curves in Model Scenarios." In Lecture Notes in Computer Science, 128–42. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82427-3_10.

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AbstractThe exchange of ideas between computer science and statistical physics has advanced the understanding of machine learning and inference significantly. This interdisciplinary approach is currently regaining momentum due to the revived interest in neural networks and deep learning. Methods borrowed from statistical mechanics complement other approaches to the theory of computational and statistical learning. In this brief review, we outline and illustrate some of the basic concepts. We exemplify the role of the statistical physics approach in terms of a particularly important contribution: the computation of typical learning curves in student teacher scenarios of supervised learning. Two, by now classical examples from the literature illustrate the approach: the learning of a linearly separable rule by a perceptron with continuous and with discrete weights, respectively. We address these prototypical problems in terms of the simplifying limit of stochastic training at high formal temperature and obtain the corresponding learning curves.
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Sala, Dariusz, and Bogusław Bieda. "Role of Stochastic Approach Applied to Life Cycle Inventory (LCI) of Rare Earth Elements (REEs) from Secondary Sources Case Studies." In Towards a Sustainable Future - Life Cycle Management, 107–20. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77127-0_10.

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AbstractMonte Carlo (MC) simulation using Crystal Ball® (CB) software is applied to life cycle inventory (LCI) modelling under uncertainty. Input data for all cases comes from the ENVIREE (ENVIronmentally friendly and efficient methods for extraction of Rare Earth Elements), i.e. from secondary sources eco-innovative project within the second ERA-NET ERA-MIN Joint Call Sustainable Supply of Raw Materials in Europe 2014. Case studies described the flotation tailings from the New Kankberg (Sweden) old gold mine and Covas (Portugal) old tungsten mine sent to re-processing/beneficiation for rare earth element (REE) recovery. In this study, we conduct the MC analysis using the CB software, which is associated with Microsoft® Excel spreadsheet model, used in order to assess uncertainty concerning cerium (Ce), lanthanum (La), neodymium (Nd) and tungsten (W) taken from Covas flotation tailings, as well as Ce, La and Nd taken from New Kankberg flotation tailings, respectively. For the current study, lognormal distribution has been assigned to La, Ce, Nd and W. In the case of Covas, the weights of each selected Ce, La, Nd and W are 32 ppm, 16 ppm, 15 ppm and 1900 ppm, respectively, whereas in the case of New Kankberg, the weights of each selected Ce, La and Nd are 170 ppm, 90 ppm and 70 ppm, respectively. For the presented case, lognormal distribution has been assigned to Ce, La, Nd and W. The results obtained from the CB, after 10,000 runs, are presented in the form of frequency charts and summary statistics. Thanks to uncertainty analysis, a final result is obtained in the form of value range. The results of this study based on the real data, and obtained using MC simulation, are more reliable than those obtained from the deterministic approach, and they have the advantage that no normality is presumed.
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Conference papers on the topic "Stochastic weights"

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Fukuda, Yasushi, and Takayuki Kawahara. "Stochastic weights binary neural networks on FPGA." In 2018 7th International Symposium on Next Generation Electronics (ISNE). IEEE, 2018. http://dx.doi.org/10.1109/isne.2018.8394726.

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Farhat, Nabil H., and Zon Yin Shae. "A Stochastic Optoelectronic Learning Machine." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1988. http://dx.doi.org/10.1364/oam.1988.pdp13.

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We report on what we believe to be the first demonstration of a fully operational optical learning machine. Learning in this machine is stochastic taking place in a self-organizing tri-layered opto-electronic neural net with plastic connectivity weights that are formed in a programmable nonvolatile spatial light modulator. The net, which can also be called a Boltzmann Learning Machine, learns by adapting its connectivity weights in accordance to environmental inputs. Learning is driven by error signals derived from statevector correlation matrices accumulated at the end of fast annealing bursts that are induced by controlled optical injection of noise into the network. Operation of the machine is made possible by two important developments in our work: Fast annealing by optically induced noisy thresholding, and stochastic learning with binary weights. Preliminary results obtained with a 24 neuron prototype show that the machine can learn, with a learning score of about 60%, to associate three 8-bit vector pairs in 10-60 minutes with relatively slow (60 msec response time) neurons and that, shifting to neurons with 1 μsec response time for example, could reduce the learning time by roughly 104 times. Methods for improving the learning score are presently under investigation and initial results are encouraging. They indicate that reducing the number of vectors to be learned by the prototype from three to two and use of on-on correlations alone in computing the coincidence probabilities and corresponding error signals controlling synaptic weight modification and adjusting the number of hidden neurons can increase the learning score to 95%. A short video of the machine in operation as it learns will be shown. We close by describing methods for constructing large-scale photonic learning machines of 103-105 neurons that utilize the concepts developed and speculate on potential applications.
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Rusnak, Rastislav, and Rudolf Jaksa. "Stochastic weights and neurons selection in neural networks for weather prediction." In 2016 IEEE 14th International Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2016. http://dx.doi.org/10.1109/sami.2016.7423034.

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Chunfu Jia. "Stochastic single machine scheduling with earliness and tardiness penalties and proportional weights." In Proceedings of 2002 American Control Conference. IEEE, 2002. http://dx.doi.org/10.1109/acc.2002.1025351.

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Tabacek, Jaroslav, and Vladimir Havlena. "Desensitized Extended Kalman Filter with Stochastic Approach to Sensitivity Reduction and Adaptive Weights." In 2022 25th International Conference on Information Fusion (FUSION). IEEE, 2022. http://dx.doi.org/10.23919/fusion49751.2022.9841381.

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Hall, T. J., W. Peiffer, M. Hands, H. Thienpont, W. A. Crossland, J. S. Shawe-Taylor, and M. van Daalen. "Considerations of the Optical and Opto-electronic Hardware Requirements for Implementation of Stochastic Bit-stream Neural Nets." In Optical Computing. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/optcomp.1995.otue18.

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The complexities of implementing neural network systems stem from the requirement that each neuron can receive excitation from many inputs (1-1000, or more) and each input must be multiplied by a weight Conventional analog and digital electronic hardware implementations of neural architectures often use much of the available hardware to implement the calculation of the product of the weights and inputs, and have to resort to a time-multiplexing scheme (which allows sharing of the multiplier hardware) to implement networks with more than a few thousand neurons in the system. This problem can be overcome by using stochastic computing techniques. Therefore, this paper details the results of an investigation of the implementation of the functional components of a stochastic bit stream neuron in optic/optoelectronic hardware. This approach offers several advantages.
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Caniou, Yves, Eddy Caron, Aurelie Kong Win Chang, and Yves Robert. "Budget-Aware Scheduling Algorithms for Scientific Workflows with Stochastic Task Weights on Heterogeneous IaaS Cloud Platforms." In 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2018. http://dx.doi.org/10.1109/ipdpsw.2018.00014.

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Zuo, Lei, and Samir A. Nayfeh. "Adaptive Least-Mean Square Feed-Forward Control With Actuator Saturation by Direct Minimization." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85494.

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The least-mean squares (LMS) adaptive feedforward algorithm is used widely for vibration and noise cancellation. If reference signals become large enough to saturate that actuators, the filter coefficients in such algorithms can diverge. The leaky LMS method limits the controller effort by augmenting the objective function by a weighted control effort, and is known to attain good performance and avoid growth of filter coefficients for well-chosen weights. We propose an algorithm that seeks to directly minimize the mean-square cost in the presence of saturation. We derive the true stochastic gradient of the cost for systems with saturation with respect to the filter coefficients and obtain an adaptation rule very close to that of the filtered-x algorithm, but in the proposed algorithm, the reference filter is a time-varying modification of the secondary channel. In simulations of an active vibration isolation system with actuator limits subject to random ground vibration, the leaky LMS algorithm attains its best performance with actuation weights small enough to allow significant actuator saturation but large enough to prevent divergence. The proposed algorithm attains performance better that attained by the leaky LMS algorithm, and does not require the selection of weights.
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Noura, A. A., and M. Nozohour. "Extension of ranking method based on effectiveness of units in society by common weights approach in stochastic DEA." In INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND STATISTICS 2013 (ICMSS2013): Proceedings of the International Conference on Mathematical Sciences and Statistics 2013. AIP, 2013. http://dx.doi.org/10.1063/1.4823896.

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Sambaturu, Prathyush, Marco Minutoli, Mahantesh Halappanavar, Ananth Kalyanaraman, and Anil Vullikanti. "Scalable and Memory-Efficient Algorithms for Controlling Networked Epidemic Processes Using Multiplicative Weights Update Method." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/717.

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We study the problem of designing scalable algorithms to find effective intervention strategies for controlling stochastic epidemic processes on networks. This is a common problem arising in agent based models for epidemic spread. Previous approaches to this problem focus on either heuristics with no guarantees or approximation algorithms that scale only to networks corresponding to county-sized populations, typically, with less than a million nodes. In particular, the mathematical-programming based approaches need to solve the Linear Program (LP) relaxation of the problem using an LP solver, which restricts the scalability of this approach. In this work, we overcome this restriction by designing an algorithm that adapts the multiplicative weights update (MWU) framework, along with the sample average approximation (SAA) technique, to approximately solve the linear program (LP) relaxation for the problem. To scale this approach further, we provide a memory-efficient algorithm that enables scaling to large networks, corresponding to country-size populations, with over 300 million nodes and 30 billion edges. Furthermore, we show that this approach provides near-optimal solutions to the LP in practice.
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Reports on the topic "Stochastic weights"

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Snyder, Victor A., Dani Or, Amos Hadas, and S. Assouline. Characterization of Post-Tillage Soil Fragmentation and Rejoining Affecting Soil Pore Space Evolution and Transport Properties. United States Department of Agriculture, April 2002. http://dx.doi.org/10.32747/2002.7580670.bard.

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Tillage modifies soil structure, altering conditions for plant growth and transport processes through the soil. However, the resulting loose structure is unstable and susceptible to collapse due to aggregate fragmentation during wetting and drying cycles, and coalescense of moist aggregates by internal capillary forces and external compactive stresses. Presently, limited understanding of these complex processes often leads to consideration of the soil plow layer as a static porous medium. With the purpose of filling some of this knowledge gap, the objectives of this Project were to: 1) Identify and quantify the major factors causing breakdown of primary soil fragments produced by tillage into smaller secondary fragments; 2) Identify and quantify the. physical processes involved in the coalescence of primary and secondary fragments and surfaces of weakness; 3) Measure temporal changes in pore-size distributions and hydraulic properties of reconstructed aggregate beds as a function of specified initial conditions and wetting/drying events; and 4) Construct a process-based model of post-tillage changes in soil structural and hydraulic properties of the plow layer and validate it against field experiments. A dynamic theory of capillary-driven plastic deformation of adjoining aggregates was developed, where instantaneous rate of change in geometry of aggregates and inter-aggregate pores was related to current geometry of the solid-gas-liquid system and measured soil rheological functions. The theory and supporting data showed that consolidation of aggregate beds is largely an event-driven process, restricted to a fairly narrow range of soil water contents where capillary suction is great enough to generate coalescence but where soil mechanical strength is still low enough to allow plastic deforn1ation of aggregates. The theory was also used to explain effects of transient external loading on compaction of aggregate beds. A stochastic forInalism was developed for modeling soil pore space evolution, based on the Fokker Planck equation (FPE). Analytical solutions for the FPE were developed, with parameters which can be measured empirically or related to the mechanistic aggregate deformation model. Pre-existing results from field experiments were used to illustrate how the FPE formalism can be applied to field data. Fragmentation of soil clods after tillage was observed to be an event-driven (as opposed to continuous) process that occurred only during wetting, and only as clods approached the saturation point. The major mechanism of fragmentation of large aggregates seemed to be differential soil swelling behind the wetting front. Aggregate "explosion" due to air entrapment seemed limited to small aggregates wetted simultaneously over their entire surface. Breakdown of large aggregates from 11 clay soils during successive wetting and drying cycles produced fragment size distributions which differed primarily by a scale factor l (essentially equivalent to the Van Bavel mean weight diameter), so that evolution of fragment size distributions could be modeled in terms of changes in l. For a given number of wetting and drying cycles, l decreased systematically with increasing plasticity index. When air-dry soil clods were slightly weakened by a single wetting event, and then allowed to "age" for six weeks at constant high water content, drop-shatter resistance in aged relative to non-aged clods was found to increase in proportion to plasticity index. This seemed consistent with the rheological model, which predicts faster plastic coalescence around small voids and sharp cracks (with resulting soil strengthening) in soils with low resistance to plastic yield and flow. A new theory of crack growth in "idealized" elastoplastic materials was formulated, with potential application to soil fracture phenomena. The theory was preliminarily (and successfully) tested using carbon steel, a ductile material which closely approximates ideal elastoplastic behavior, and for which the necessary fracture data existed in the literature.
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