Zobrazeno 1 - 10
of 4 456
pro vyhledávání: '"Ramírez, Juan A."'
Autor:
Díaz-Aranda, Sergio, Ramírez, Juan Marcos, Daga, Mohit, Champati, Jaya Prakash, Aguilar, José, Lillo, Rosa Elvira, Anta, Antonio Fernández
Epidemiologists and social scientists have used the Network Scale-Up Method (NSUM) for over thirty years to estimate the size of a hidden sub-population within a social network. This method involves querying a subset of network nodes about the number
Externí odkaz:
http://arxiv.org/abs/2407.10640
Stellar mass, binary black hole (BBH) mergers dominates the sources of gravitational wave (GW) events so far detected the LIGO/Virgo/KAGRA (LVK) experiment. The origin of these BBHs is unknown, and no electromagnetic (EM) counterpart has been undoubt
Externí odkaz:
http://arxiv.org/abs/2407.09945
Autor:
Sohrabi, Motahareh, Ramirez, Juan, Zhang, Tianyue H., Lacoste-Julien, Simon, Gallego-Posada, Jose
Constrained optimization offers a powerful framework to prescribe desired behaviors in neural network models. Typically, constrained problems are solved via their min-max Lagrangian formulations, which exhibit unstable oscillatory dynamics when optim
Externí odkaz:
http://arxiv.org/abs/2406.04558
Autor:
Tiba, Azzeddine, Dairay, Thibault, de Vuyst, Florian, Mortazavi, Iraj, Ramirez, Juan-Pedro Berro
Stable partitioned techniques for simulating unsteady fluid-structure interaction (FSI) are known to be computationally expensive when high added-mass is involved. Multiple coupling strategies have been developed to accelerate these simulations, but
Externí odkaz:
http://arxiv.org/abs/2405.09941
Autor:
Hashemizadeh, Meraj, Ramirez, Juan, Sukumaran, Rohan, Farnadi, Golnoosh, Lacoste-Julien, Simon, Gallego-Posada, Jose
Model pruning is a popular approach to enable the deployment of large deep learning models on edge devices with restricted computational or storage capacities. Although sparse models achieve performance comparable to that of their dense counterparts
Externí odkaz:
http://arxiv.org/abs/2310.20673
Autor:
Srivastava, Ajitesh, Ramírez, Juan Marcos, Díaz-Aranda, Sergio, Aguilar, Jose, Ortega, Antonio, Anta, Antonio Fernández, Lillo, Rosa Elvira
Indirect surveys, in which respondents provide information about other people they know, have been proposed for estimating (nowcasting) the size of a \emph{hidden population} where privacy is important or the hidden population is hard to reach. Examp
Externí odkaz:
http://arxiv.org/abs/2307.06643
Stochastic min-max optimization has gained interest in the machine learning community with the advancements in GANs and adversarial training. Although game optimization is fairly well understood in the deterministic setting, some issues persist in th
Externí odkaz:
http://arxiv.org/abs/2306.07905
Autor:
Tiba, Azzeddine, Dairay, Thibault, de Vuyst, Florian, Mortazavi, Iraj, Ramirez, Juan-Pedro Berro
The main goal of this work is to develop a data-driven Reduced Order Model (ROM) strategy from high-fidelity simulation result data of a Full Order Model (FOM). The goal is to predict at lower computational cost the time evolution of solutions of Flu
Externí odkaz:
http://arxiv.org/abs/2306.07570
The performance of trained neural networks is robust to harsh levels of pruning. Coupled with the ever-growing size of deep learning models, this observation has motivated extensive research on learning sparse models. In this work, we focus on the ta
Externí odkaz:
http://arxiv.org/abs/2208.04425
Autor:
Ramirez, Juan, Gallego-Posada, Jose
Advances in Implicit Neural Representations (INR) have motivated research on domain-agnostic compression techniques. These methods train a neural network to approximate an object, and then store the weights of the trained model. For example, given an
Externí odkaz:
http://arxiv.org/abs/2207.04144