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pro vyhledávání: '"Langlois, Gabriel P."'
Maximum entropy (Maxent) models are a class of statistical models that use the maximum entropy principle to estimate probability distributions from data. Due to the size of modern data sets, Maxent models need efficient optimization algorithms to sca
Externí odkaz:
http://arxiv.org/abs/2403.06816
Topology optimization, a technique to determine where material should be placed within a predefined volume in order to minimize a physical objective, is used across a wide range of scientific fields and applications. A general application for topolog
Externí odkaz:
http://arxiv.org/abs/2306.12555
Autor:
Darbon, Jérôme, Langlois, Gabriel P.
Logistic regression is a widely used statistical model to describe the relationship between a binary response variable and predictor variables in data sets. It is often used in machine learning to identify important predictor variables. This task, va
Externí odkaz:
http://arxiv.org/abs/2111.15426
Autor:
Darbon, Jérôme, Langlois, Gabriel P.
The linear primal-dual hybrid gradient (PDHG) method is a first-order method that splits convex optimization problems with saddle-point structure into smaller subproblems. Unlike those obtained in most splitting methods, these subproblems can general
Externí odkaz:
http://arxiv.org/abs/2109.12222
Many imaging problems can be formulated as inverse problems expressed as finite-dimensional optimization problems. These optimization problems generally consist of minimizing the sum of a data fidelity and regularization terms. In [23,26], connection
Externí odkaz:
http://arxiv.org/abs/2104.11285
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 January 2024 418 Part A
Autor:
Darbon, Jerome, Langlois, Gabriel P.
Variational and Bayesian methods are two approaches that have been widely used to solve image reconstruction problems. In this paper, we propose original connections between Hamilton--Jacobi (HJ) partial differential equations and a broad class of Ba
Externí odkaz:
http://arxiv.org/abs/2003.05572
We propose new and original mathematical connections between Hamilton-Jacobi (HJ) partial differential equations (PDEs) with initial data and neural network architectures. Specifically, we prove that some classes of neural networks correspond to repr
Externí odkaz:
http://arxiv.org/abs/1910.09045
We construct a class of spatially polynomial velocity fields that are exact solutions of the planar unsteady Navier-Stokes equation. These solutions can be used as simple benchmarks for testing numerical methods or verifying the feasibility of flow-f
Externí odkaz:
http://arxiv.org/abs/1908.04657
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