Zobrazeno 1 - 10
of 361
pro vyhledávání: '"Tran, Quang Huy"'
Numerical simulations are a highly valuable tool to evaluate the impact of the uncertainties of various modelparameters, and to optimize e.g. injection-production scenarios in the context of underground storage (of CO2typically). Finite volume approx
Externí odkaz:
http://arxiv.org/abs/2406.07950
Gromov-Wasserstein distance has found many applications in machine learning due to its ability to compare measures across metric spaces and its invariance to isometric transformations. However, in certain applications, this invariance property can be
Externí odkaz:
http://arxiv.org/abs/2307.10093
Publikováno v:
Tạp chí Khoa học, Vol 53, Iss 3A, Pp 16-22 (2024)
Learning sentence representation with the full semantics of a document is a challenge in natural language processing problems because if the semantic representation vector of the sentence is suitable, it will increase the performance of finding simil
Externí odkaz:
https://doaj.org/article/bb4c176f0c1f4927b1e93be511e0ff5c
Autor:
Tran Quang-Huy, Bhisham Sharma, Luong Thi Theu, Duc-Tan Tran, Subrata Chowdhury, Chandran Karthik, Saravanakumar Gurusamy
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-21 (2024)
Abstract The distorted Born iterative (DBI) method is considered to obtain images with high-contrast and resolution. Besides satisfying the Born approximation condition, the frequency-hopping (FH) technique is necessary to gradually update the sound
Externí odkaz:
https://doaj.org/article/537c03ed7d9248b4b281d86107f1373c
Autor:
Koroko, Abdoulaye, Anciaux-Sedrakian, Ani, Gharbia, Ibtihel Ben, Garès, Valérie, Haddou, Mounir, Tran, Quang Huy
As a second-order method, the Natural Gradient Descent (NGD) has the ability to accelerate training of neural networks. However, due to the prohibitive computational and memory costs of computing and inverting the Fisher Information Matrix (FIM), eff
Externí odkaz:
http://arxiv.org/abs/2303.18083
Autor:
Tran, Quang Huy, Janati, Hicham, Courty, Nicolas, Flamary, Rémi, Redko, Ievgen, Demetci, Pinar, Singh, Ritambhara
Optimal transport (OT) compares probability distributions by computing a meaningful alignment between their samples. CO-optimal transport (COOT) takes this comparison further by inferring an alignment between features as well. While this approach lea
Externí odkaz:
http://arxiv.org/abs/2205.14923
Parametrization and Cartesian representation techniques for robust resolution of chemical equilibria
Publikováno v:
In Journal of Computational Physics 1 February 2025 522
Autor:
Koroko, Abdoulaye, Anciaux-Sedrakian, Ani, Gharbia, Ibtihel Ben, Garès, Valérie, Haddou, Mounir, Tran, Quang Huy
Several studies have shown the ability of natural gradient descent to minimize the objective function more efficiently than ordinary gradient descent based methods. However, the bottleneck of this approach for training deep neural networks lies in th
Externí odkaz:
http://arxiv.org/abs/2201.10285
Optimal transport (OT) theory underlies many emerging machine learning (ML) methods nowadays solving a wide range of tasks such as generative modeling, transfer learning and information retrieval. These latter works, however, usually build upon a tra
Externí odkaz:
http://arxiv.org/abs/2110.00629
This paper is concerned with the Richards equation in a heterogeneous domain, each subdomain of which is homogeneous and represents a rocktype. Our first contribution is to rigorously prove convergence toward a weak solution of cell-centered finite-v
Externí odkaz:
http://arxiv.org/abs/2101.08077