Results 21 to 30 of about 6,455,829 (284)
Natural gradients for deformable registration [PDF]
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010 We apply the concept of natural gradients to deformable registration. The motivation stems from the lack of physical interpretation for gradients of image-based difference measures. The main idea is to endow the space of deformations with a distance metric which reflects the variation of the difference measure between two deformations.
Darko Zikic, Ali Kamen, Nassir Navab
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Adaptive Natural Gradient Method for Learning of Stochastic Neural Networks in Mini-Batch Mode
Applied Sciences, 2019 Gradient descent method is an essential algorithm for learning of neural networks. Among diverse variations of gradient descent method that have been developed for accelerating learning speed, the natural gradient learning is based on the theory of ...
Hyeyoung Park, Kwanyong Lee
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An approach to nonlinear viscoelasticity via metric gradient flows [PDF]
, 2013 We formulate quasistatic nonlinear finite-strain viscoelasticity of rate type as a gradient system. Our focus is on nonlinear dissipation functionals and distances that are related to metrics on weak diffeomorphisms and that ensure time-dependent frame ...
Mielke, A. +4 more
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Natural Evolutionary Gradient Descent Strategy for Variational Quantum Algorithms
Intelligent Computing, 2023 Recent research has demonstrated that parametric quantum circuits (PQCs) are affected by gradients that progressively vanish to zero as a function of the number of qubits.
Jianshe Xie +4 more
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Optimising Distributions with Natural Gradient Surrogates
CoRR, 2023 Natural gradient methods have been used to optimise the parameters of probability distributions in a variety of settings, often resulting in fast-converging procedures. Unfortunately, for many distributions of interest, computing the natural gradient has a number of challenges.
Jonathan So, Richard E. Turner
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Entropy, 2017 Information geometry enables a deeper understanding of the methods of statistical inference. In this paper, the problem of nonlinear parameter estimation is considered from a geometric viewpoint using a natural gradient descent on statistical manifolds ...
Yongqiang Cheng, Xuezhi Wang, Bill Moran
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Mathematics, 2022 The high accuracy attainment, using less complex architectures of neural networks, remains one of the most important problems in machine learning. In many studies, increasing the quality of recognition and prediction is obtained by extending neural ...
Ruslan Abdulkadirov +2 more
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Research progress in biomimetic gradient scaffolds for osteochondral tissue engineering
Cailiao gongcheng, 2022 Osteochondral defects are the main cause of joint morbidity and disability in elderly patients, and osteochondral tissue engineering is one of the methods to repair osteochondral defects.
WAN Li +5 more
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On the Fisher Metric of Conditional Probability Polytopes
Entropy, 2014 We consider three different approaches to define natural Riemannian metrics on polytopes of stochastic matrices. First, we define a natural class of stochastic maps between these polytopes and give a metric characterization of Chentsov type in terms of ...
Guido Montúfar, Johannes Rauh, Nihat Ay
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On Performance and Calibration of Natural Gradient Langevin Dynamics
IEEE Access, 2023 Producing deep neural network (DNN) models with calibrated confidence is essential for applications in many fields, such as medical image analysis, natural language processing, and robotics.
Hanif Amal Robbani +3 more
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