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Showing 1 to 9 of 9 for “"IMPLICIT FUNCTION THEOREM"”.
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O teorema da função implícita em espaços de Banach e aplicações.
… study and make a historical overview about the implicit function theorem. our main objective will be to present the generalization of this theorem for Banach spaces and prove in detail that extension. Furthermore, we will show some results of extreme usefulness that will be demonstrated as …
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Computational Methods for Sensitivity Analysis with Applications to Elliptic Boundary Value Problems
… along with the method of mappings and the Implicit Function Theorem. Examples are given which illustrate the use of the framework, and some of the shortcomings of the theory are also identified. An overview of some computational methods which make use of the method of mappings is also …
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Adaptive neural network control of discrete-time nonlinear systems
… is proved. For non-affine systems, by using implicit function theorem, the existence of the implicit desired feedback controls is also proved. Single layer neural networks, such as radial basis function neural networks, high order neural networks, and multi layer neural networks are used as …
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Intrinsic Differentiability and Intrinsic Regular Surfaces in Carnot groups
… as a non critical level set of a C^1 intrinsic function. In a similar way, a k-codimensional intrinsic regular surface is locally defined as a non critical level set of a C^1 intrinsic vector function. Through Implicit Function Theorem, S can be locally represented as an intrinsic graph by a …
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Probabilistic machine learning algorithms for molecule discovery
… 3) and adaptive deep kernel fitting with implicit function theorem (chapter 4) are both algorithms which use a Gaussian process with a deep neural network kernel function to model the relationship between molecular structure and some property of interest. Tanimoto random features (chapter …
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Comparative Statics Analysis of Some Operations Management Problems
… a parametric analysis followed by the use of the implicit function theorem. Providing a rigorous framework for comparative statics analysis, which can be applied to other problems that are not amenable to traditional parametric analysis, is our main contribution. We demonstrate this approach on …
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Bridging Deep Learning and Probabilistic Inference: Towards Data Efficiency, Identifiability, and Sampling Scalability
… optimisation framework and solving it with the implicit function theorem, this approach enhances the generalisation capabilities of deep neural networks for few-shot molecular property prediction and optimisation tasks. Next, we analyse the theoretical properties of neural network …
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ΝΕΕΣ ΜΕΘΟΔΟΙ ΕΠΙΛΥΣΗΣ ΣΥΣΤΗΜΑΤΩΝ ΜΗ ΓΡΑΜΜΙΚΩΝ ΑΛΓΕΒΡΙΚΩΝ Η/ΚΑΙ ΥΠΕΡΒΑΤΙΚΩΝ ΕΞΙΣΩΣΕΩΝ
… OF THE SOLUTION AND IT DOES NOT DIRECTLY NEED FUNCTION EVALUATIONS. A PROOF OF CONVERGENCE IS ALSO GIVEN. NEXT, THE ABOVE METHOD IN GENERALIZED AND A NEW ONE IS DEVELOPED FOR THE NUMERICAL SOLUTION OF SYSTEMS OF NONLINEAR EQUATIONS IN IRN. SUBSEQUENTLY, A PROCEDURE IS INTRODUCED WHICH …