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Results 1 - 10
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202
A Global Optimization Approach to Robust Multi-Model Fitting
"... We present a novel Quadratic Program (QP) formulation for robust multi-model fitting of geometric structures in vision data. Our objective function enforces both the fidelity of a model to the data and the similarity between its associated inliers. Departing from most previous optimizationbased appr ..."
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Cited by 4 (2 self)
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We present a novel Quadratic Program (QP) formulation for robust multi-model fitting of geometric structures in vision data. Our objective function enforces both the fidelity of a model to the data and the similarity between its associated inliers. Departing from most previous optimizationbased
Dynamical Modeling and Multi-Experiment Fitting with
"... Motivation: Modelers in Systems Biology need a flexible framework that allows them to easily create new dynamic models, investigate their properties and fit several experimental data sets simultaneously. Multi-experiment-fitting is a powerful approach to estimate parameter values, to check the valid ..."
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Motivation: Modelers in Systems Biology need a flexible framework that allows them to easily create new dynamic models, investigate their properties and fit several experimental data sets simultaneously. Multi-experiment-fitting is a powerful approach to estimate parameter values, to check
Multi-population mortality models: Fitting, Forecasting and Comparisons
"... We review a number of multi-population mortality models: variations of the Li and Lee (2005) model, and the common-age-effect (CAE) model of Kleinow (2014). Model parameters are es-timated using maximum likelihood. Although this introduces some challenging identifiability problems and complicates th ..."
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and develop forecasting models that produce non-diverging, joint mortality rate scenarios. It is found that the CAE model fits best. But we also find that the Li and Lee model potentially suffers from robustness problems when calibrated using maximum likelihood.
Robust discriminative response map fitting with constrained local models", CVPR
, 2013
"... We present a novel discriminative regression based ap-proach for the Constrained Local Models (CLMs) frame-work, referred to as the Discriminative Response Map Fit-ting (DRMF) method, which shows impressive performance in the generic face fitting scenario. The motivation behind this approach is that ..."
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Cited by 38 (12 self)
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We present a novel discriminative regression based ap-proach for the Constrained Local Models (CLMs) frame-work, referred to as the Discriminative Response Map Fit-ting (DRMF) method, which shows impressive performance in the generic face fitting scenario. The motivation behind this approach
Fitting and Estimating Parameter Confidence Limits with
"... Abstract—Sherpa is a generalized modeling and fitting package. Primarily developed for the Chandra Interactive Analysis of Observations (CIAO) package by the Chandra X-ray Center, Sherpa provides an Object-Oriented Programming (OOP) API for parametric data modeling. It is designed to use the forward ..."
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the forward fitting technique to search for the set of best-fit parameter values in parametrized model functions. Sherpa can also estimate the confidence limits on best-fit parameters using a new confidence method or using an algorithm based on Markov chain Monte Carlo (MCMC). Confidence limits on parameter
A Two-Step Estimation Procedure and a Goodness-of-Fit Test for Spatial Extremes Models
"... Parametric max-stable processes are increasingly used to model spatial extremes. Since the dependence structure is specified for block maxima, the data used for inference are block maxima from all sites. To improve the estimation efficiency, we propose a two-step approach with composite likelihood t ..."
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and quantile-quantile plot. We proposed a goodness-of-fit test for max-stable processes based on the comparison between a nonparametric and a parametric estimator of the corresponding unknown multivariate Pickands dependence function. The proposed two-step procedure separates the estimation of marginal
GENETIC EXPONENTIALLY FITTED METHOD FOR SOLVING MULTI-DIMENSIONAL DRIFT-DIFFUSION EQUATIONS
"... Abstract. A general approach was proposed in this article to develop high-order exponentially fitted basis functions for finite element approximations of multi-dimensional drift-diffusion equations for modeling biomolecular electrodiffusion processes. Such methods are highly desirable for achieving ..."
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Abstract. A general approach was proposed in this article to develop high-order exponentially fitted basis functions for finite element approximations of multi-dimensional drift-diffusion equations for modeling biomolecular electrodiffusion processes. Such methods are highly desirable for achieving
An Exponentially Fitted Finite Element Scheme for Diffusion Process Simulation on Coarse Grids
, 1995
"... A new finite element scheme for diffusion process simulation, which allows coarse grid spacings in the areas of exponentially varying concentrations and fluxes, is proposed. It employs a nonlinear test function obtained from local divergence free conditions. Two-dimensional test computations show cl ..."
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A new finite element scheme for diffusion process simulation, which allows coarse grid spacings in the areas of exponentially varying concentrations and fluxes, is proposed. It employs a nonlinear test function obtained from local divergence free conditions. Two-dimensional test computations show
On the Benefits of Divergent Search for Evolved Representations
"... Abstract—Evolved representations in evolutionary computation are often fragile, which can impede representation-dependent mechanisms such as self-adaptation. In contrast, evolved representations in nature are robust, evolvable, and creatively exploit available representational features. This paper p ..."
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Cited by 1 (1 self)
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eventually converge to a point in the search space that locally maximizes the fitness function. The problem is that individuals that maximize fitness do not need good representations because a representation’s future potential is not reflected by its current fitness. In contrast, search methods without
Rapid global fitting of large fluorescence lifetime imaging microscopy datasets
"... Fluorescence lifetime imaging (FLIM) is widely applied to obtain quantitative information from fluorescence signals, particularly using Förster Resonant Energy Transfer (FRET) measurements to map, for example, protein-protein interactions. Extracting FRET efficiencies or population fractions typica ..."
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Cited by 1 (1 self)
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robustly extracted using global analysis to simultaneously fit the fluorescence decay data of all pixels in an image or dataset to a multi-exponential model under the assumption that the lifetime components are invariant across the image (dataset). This approach is often considered to be prohibitively slow
Results 1 - 10
of
202
