Results 11 to 20 of about 7,757,311 (314)
Non‐parametric regression for networks [PDF]
Stat, 2021 Network data are becoming increasingly available, and so there is a need to develop a suitable methodology for statistical analysis. Networks can be represented as graph Laplacian matrices, which are a type of manifold‐valued data. Our main objective is to estimate a regression curve from a sample of graph Laplacian matrices conditional on a set of ...
Katie E. Severn +2 more
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Non-parametric parametricity [PDF]
Journal of Functional Programming, 2009 Abstract Type abstraction and intensional type analysis are features seemingly at odds—type abstraction is intended to guarantee parametricity and representation independence, while type analysis is inherently non-parametric. Recently, however, several researchers have proposed and implemented “dynamic type generation” as a way to ...
Neis, G., Dreyer, D., Rossberg, A.
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Non-Parametric Outlier Synthesis
CoRR, 2023 Out-of-distribution (OOD) detection is indispensable for safely deploying machine learning models in the wild. One of the key challenges is that models lack supervision signals from unknown data, and as a result, can produce overconfident predictions on OOD data.
Leitian Tao +3 more
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Non-parametric random simulation results. [PDF]
, 2022 Non-parametric random simulation results.
Liping Zhai (13844318) +3 more
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On non‐parametric fatigue optimization
International Journal for Numerical Methods in Engineering, 2022 AbstractThe present work presents a novel approach for semi‐analytic adjoint sensitivity‐based design optimization for nonproportional fatigue damage. In order to apply fatigue damage in sensitivity‐based design optimizations, an essential part is to calculate correct sensitivities. However, this is not straight forward since fatigue damage calculation
Roman Sartorti +3 more
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Non-parametric Dependent Components [PDF]
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2006 Canonical correlation analysis (CCA) is equivalent to finding mutual information-maximizing projections for normally distributed data. We remove the restriction of normality by non-parametric estimation, and formulate the problem of finding dependent components with a connection to Bayes factors. The method is applied for characterizing yeast stress by
Arto Klami, Samuel Kaski
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Non-Parametric Calibration for Classification [PDF]
CoRR, 2019 Many applications of classification methods not only require high accuracy but also reliable estimation of predictive uncertainty. However, while many current classification frameworks, in particular deep neural networks, achieve high accuracy, they tend to incorrectly estimate uncertainty. In this paper, we propose a method that adjusts the confidence
Wenger, Jonathan +2 more
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CoRR, 2022 Recent feed-forward neural methods of arbitrary image style transfer mainly utilized encoded feature map upto its second-order statistics, i.e., linearly transformed the encoded feature map of a content image to have the same mean and variance (or covariance) of a target style feature map.
Jeong-Sik Lee, Hyun-Chul Choi
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Frontiers in Genetics, 2019 There is a paradigm shift from the traditional focus on the “average” individual towards the definition and analysis of trait variation within individual life-history and among individuals in populations.
Joao A.N. Filipe, Ilias Kyriazakis
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Explaining predictive models using Shapley values and non-parametric vine copulas
Dependence Modeling, 2021 In this paper the goal is to explain predictions from complex machine learning models. One method that has become very popular during the last few years is Shapley values.
Aas Kjersti +3 more
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