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554,467
Infinitehorizon policygradient estimation
 Journal of Artificial Intelligence Research
, 2001
"... Gradientbased approaches to direct policy search in reinforcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in valuefunction methods. In this paper we introduce � � , a si ..."
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Cited by 208 (5 self)
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simulationbased algorithm for generating a biased estimate of the gradient of the average reward in Partially Observable Markov Decision Processes ( � s) controlled by parameterized stochastic policies. A similar algorithm was proposed by Kimura, Yamamura, and Kobayashi (1995). The algorithm’s chief
Gradient estimation in uncertain data
 MVA'98, IAPR Workshop on Machine Vision Applications, Makuhari
, 1998
"... Detecting edges in images which are distorted by unreliable or missing data samples can be done using normalized (differential) convolution. This work presents a comparison between gradient estimation using normalized convolution and gradient estimation using normalized differential convolution with ..."
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Cited by 2 (1 self)
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Detecting edges in images which are distorted by unreliable or missing data samples can be done using normalized (differential) convolution. This work presents a comparison between gradient estimation using normalized convolution and gradient estimation using normalized differential convolution
Pegasos: Primal Estimated subgradient solver for SVM
"... We describe and analyze a simple and effective stochastic subgradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy ɛ is Õ(1/ɛ), where each iteration operates on a singl ..."
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Cited by 541 (21 self)
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We describe and analyze a simple and effective stochastic subgradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy ɛ is Õ(1/ɛ), where each iteration operates on a
Quantum computational gradient estimation
, 2005
"... Classically, determining the gradient of a blackbox function f: R p → R requires p + 1 evaluations. Using the quantum Fourier transform, two evaluations suffice. This is based on the approximate local periodicity of e 2πiλf(x). It is shown that sufficiently precise machine arithmetic results in gra ..."
Gradient Estimation Revitalized
 IEEE Trans. Visualization and Computer Graphics
, 2010
"... and shaded by interpolating the gradient obtained through a fourthorder central differencing scheme (pFIR). Right: The middle dataset shaded using our proposed fourthorder shifted gradient estimation scheme (pFIRs). Notice how the details in the bones and skull are much better preserved as compar ..."
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Cited by 7 (3 self)
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and shaded by interpolating the gradient obtained through a fourthorder central differencing scheme (pFIR). Right: The middle dataset shaded using our proposed fourthorder shifted gradient estimation scheme (pFIRs). Notice how the details in the bones and skull are much better preserved
Gradient estimates for a degenerate . . .
, 2007
"... Qualitative properties of nonnegative solutions to a quasilinear degenerate parabolic equation with an absorption term depending solely on the gradient are shown, providing information on the competition between the nonlinear diffusion and the nonlinear absorption. In particular, the limit as t → ..."
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as t → ∞ of the L 1norm of integrable solutions is identified, together with the rate of expansion of the support for compactly supported initial data. The persistence of dead cores is also shown. The proof of these results strongly relies on gradient estimates which are first established.
Appendix A Gradient estimation
"... gradient at any time can be computed numerically . use nonparametric regression to estimate a biascorrecting curve (the mean exact gradient as a function of the estimated gradients) . unbias the original gradient estimates by applying the estimated biascorrecting curve. The bias correction i ..."
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gradient at any time can be computed numerically . use nonparametric regression to estimate a biascorrecting curve (the mean exact gradient as a function of the estimated gradients) . unbias the original gradient estimates by applying the estimated biascorrecting curve. The bias correction
Stochastic Gradient Estimation
, 2006
"... We consider the problem of efficiently estimating gradients from stochastic simulation. Although the primary motivation is their use in simulation optimization, the resulting estimators can also be useful in other ways, e.g., sensitivity analysis. The main approaches described are finite differences ..."
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Cited by 39 (6 self)
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We consider the problem of efficiently estimating gradients from stochastic simulation. Although the primary motivation is their use in simulation optimization, the resulting estimators can also be useful in other ways, e.g., sensitivity analysis. The main approaches described are finite
Transport inequalities, gradient estimates, entropy and Ricci curvature
 Comm. Pure Appl. Math
"... Abstract. We present various characterizations of uniform lower bounds for the Ricci curvature of a smooth Riemannian manifold M in terms of convexity properties of the entropy (considered as a function on the space of probability measures on M ) as well as in terms of transportation inequalities f ..."
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Cited by 131 (3 self)
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for volume measures, heat kernels and Brownian motions and in terms of gradient estimates for the heat semigroup.
Gradient estimation in global optimization algorithms
 Congress on Evolutionary Computation
, 2009
"... Abstract — The role of gradient estimation in global optimization is investigated. The concept of a regional gradient is introduced as a tool for analyzing and comparing different types of gradient estimates. The correlation of different estimated gradients to the direction of the global optima is e ..."
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Cited by 2 (1 self)
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Abstract — The role of gradient estimation in global optimization is investigated. The concept of a regional gradient is introduced as a tool for analyzing and comparing different types of gradient estimates. The correlation of different estimated gradients to the direction of the global optima
Results 1  10
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554,467