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Gradient analysis
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#	Item
311	Robot Learning with State-Dependent Exploration Thomas R¨uckstieß, Martin Felder, Frank Sehnke, J¨urgen Schmidhuber Abstract— Policy gradient algorithms are among the few learning methods successfully applied to dem Add to Reading List Source URL: www6.in.tum.de Language: English - Date: 2013-05-15 08:23:46 Reinforcement learning Normal distribution Artificial neural network Dimensional analysis Likelihood function Statistics Estimation theory Machine learning
312	VARIATIONAL BELTRAMI FLOWS OVER MANIFOLDS Nir Sochen ∗ Rachid Deriche and Lucero Lopez-Perez Dept. of Applied Mathematics Add to Reading List Source URL: www.math.tau.ac.il Language: English - Date: 2004-12-26 08:20:28 Differential geometry Topology Manifold Tangent space Gradient Orbifold Itō diffusion Mathematical analysis Differential topology Mathematics
313	Package ‘CoxBoost’ July 2, 2014 Version 1.4 Title Cox models by likelihood based boosting for a single survival endpoint or competing risks Author Harald Binder Maintainer Harald Binder Add to Reading List Source URL: cran.r-project.org Language: English - Date: 2014-07-02 09:38:46 Machine learning Cross-validation Model selection Degrees of freedom Proportional hazards models Gradient boosting Boosting Null Matrix Statistics Regression analysis Ensemble learning
314	Ma1c 2010 Homework 2 Solutions Problem 1 a. Assume that f ′ (x; y) = 0 for every x in some n-ball B(a) and for every vector y. Use the mean-value theorem to prove that f is constant on B(a). b. Suppose that f ′ (x; Add to Reading List Source URL: math.caltech.edu Language: English - Date: 2010-04-09 11:18:32 Differential calculus Vector calculus Derivative Mean value theorem Partial derivative Gradient Infinite-dimensional holomorphy Chain rule Mathematical analysis Calculus Mathematics
315	Package ‘GPArotation’ November 25, 2014 Version[removed]Title GPA Factor Rotation Description Gradient Projection Algorithm Rotation for Factor Analysis. See ?GPArotation.Intro for more details. Depends R (>= 2.0.0 Add to Reading List Source URL: cran.r-project.org Language: English - Date: 2014-11-25 02:40:18 Mathematics Matrices Varimax rotation Factor analysis Orthogonal matrix Matrix Rotation matrix Rotation Euclidean vector Algebra Statistics Multivariate statistics
316	Practice Problems on Gradients and Directional Derivatives p 1) Consider the function f (x, y) = ln( x2 + y 2 ). Find its gradient at the point (x, y) = (1, −1). At this point, what is the directional derivative of f i Add to Reading List Source URL: www.econ.ucsb.edu Language: English - Date: 2009-11-09 20:08:47 Analytic geometry Differential calculus Elementary mathematics Vector calculus Generalizations of the derivative Gradient Derivative Slope Dimensional analysis Mathematics Mathematical analysis Calculus
317	Parametric Policy Gradients for Robotics Frank Sehnke, Thomas R¨uckstieß, Martin Felder and J¨urgen Schmidhuber Abstract— Slow convergence is a major problem for policy gradient methods. It is a consequence of the f Add to Reading List Source URL: www6.in.tum.de Language: English - Date: 2013-05-15 08:23:49 Estimation theory Dimensional analysis CMA-ES Applied mathematics Science Measurement Statistics Reinforcement learning
318	HW 2 , MA 1C PRAC[removed]Evaluate the partial derivatives ∂z/∂x and ∂z/∂y for the given function at the p indicated points: (a) z = a2 − x2 − y 2 ; (0, 0), (a/2, a/2) Add to Reading List Source URL: www.math.caltech.edu Language: English - Date: 2014-04-10 15:41:17 Differential calculus Functions and mappings Integral calculus Coordinate systems Multivariable calculus Derivative Partial derivative Integration by substitution Gradient Calculus Mathematical analysis Mathematics
319	Solution to HW 4, Ma 1c Prac[removed]Remark : every function appearing in this homework set is sufficiently nice– at least C 3 following the jargon from the textbook–we can apply all kinds of theorems from the textbook Add to Reading List Source URL: www.math.caltech.edu Language: English - Date: 2014-04-28 18:05:01 Vector calculus Vector field Arc length Gradient Envelope Method of characteristics Mathematical analysis Calculus Mathematics
320	Chapter 3 Tangent spaces, normals and extrema If S is a surface in 3-space, with a point a ∈ S where S looks smooth, i.e., without any fold or cusp or self-crossing, we can intuitively define the tangent plane to S at Add to Reading List Source URL: math.caltech.edu Language: English - Date: 2010-04-14 12:27:36 Differential geometry Vector calculus Differential topology Differential calculus Multivariable calculus Tangent space Normal Gradient Vector field Mathematical analysis Mathematics Calculus

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