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Parametric modeling of visual search efficiency in real scenes.
PLoS ONE, 2015 How should the efficiency of searching for real objects in real scenes be measured? Traditionally, when searching for artificial targets, e.g., letters or rectangles, among distractors, efficiency is measured by a reaction time (RT) × Set Size function ...
Xing Zhang +4 more
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Parametric Models of Local Search Progression [PDF]
SSRN Electronic Journal, 2008 AbstractAlgorithms that search for good solutions to optimization problems present a trace of current best objective values over time. We describe an empirical study of parametric models of this progression that are both interesting as ways to characterize the search progression compactly and useful as means of predicting search behavior.
Johan Oppen, David L. Woodruff
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Non-parametric contextual stochastic search [PDF]
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016 Stochastic search algorithms are black-box optimizer of an objective function. They have recently gained a lot of attention in operations research, machine learning and policy search of robot motor skills due to their ease of use and their generality.
Abdolmaleki, A. +3 more
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Cole's Parametric Search Technique Made Practical [PDF]
, 2013 Parametric search has been widely used in geometric algorithms. Cole's improvement provides a way of saving a logarithmic factor in the running time over what is achievable using the standard method. Unfortunately, this improvement comes at the expense of making an already complicated algorithm even more complex; hence, this technique has been mostly ...
Michael T. Goodrich, Paweł Pszona
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Mathematical model for the parametric study of ammonia propulsion system in microsatellite platform by random search method [PDF]
Омский научный вестник: Серия "Авиационно-ракетное и энергетическое машиностроение", 2018 A large number of basic design parameters of the ammonia propulsion system makes the task of their search by the method of random search urgent.
V. N. Blinov +2 more
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Non-parametric policy search with limited information loss [PDF]
, 2017 Learning complex control policies from non-linear and redundant sensory input is an important challenge for reinforcement learning algorithms. Non-parametric methods that approximate values functions or transition models can address this problem, by adapting to the complexity of the data set. Yet, many current non-parametric approaches rely on unstable
Herke van Hoof +2 more
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Parametric and nonparametric inference in equilibrium job search models [PDF]
, 2008 Equilibrium job search models allow for labor markets with homogeneous workers and firms to yield nondegenerate wage densities. However, the resulting wage densities do not accord well with empirical regularities. Accordingly, many extensions to the basic equilibrium search model have been considered (e.g., heterogeneity in productivity, heterogeneity ...
Koop, Gary
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Efficient Non-Parametric Optimizer Search for Diverse Tasks [PDF]
, 2022 Efficient and automated design of optimizers plays a crucial role in full-stack AutoML systems. However, prior methods in optimizer search are often limited by their scalability, generability, or sample efficiency. With the goal of democratizing research and application of optimizer search, we present the first efficient, scalable and generalizable ...
Ruochen Wang +3 more
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Prognostic value of CMR parametric mapping in cardiac amyloidosis: an updated systematic review and meta-analysis [PDF]
Open HeartBackground Cardiac amyloidosis (CA) is the leading cause of mortality in systemic amyloidosis, highlighting the need for accurate risk assessment to guide patient management.
Adam Ioannou +6 more
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Applied Sciences, 2023 Odometry is a computation method that provides a periodic estimation of the relative displacements performed by a mobile robot based on its inverse kinematic matrix, its previous orientation and position, and the estimation of the angular rotational ...
Jordi Palacín +3 more
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