Zobrazeno 1 - 10
of 270
pro vyhledávání: '"Tejedor, Javier"'
Autor:
Billhardt, Holger, Fernández, Alberto, Martí, Pasqual, Tejedor, Javier Prieto, Ossowski, Sascha
Publikováno v:
Electronics, Volume 11, Issue 4 (2022)
One of the main problems that local authorities of large cities have to face is the regulation of urban mobility. They need to provide the means to allow for the efficient movement of people and distribution of goods. However, the provisioning of tra
Externí odkaz:
http://arxiv.org/abs/2401.12329
Quantum federated learning (QFL) is a quantum extension of the classical federated learning model across multiple local quantum devices. An efficient optimization algorithm is always expected to minimize the communication overhead among different qua
Externí odkaz:
http://arxiv.org/abs/2303.08116
This work focuses on designing low complexity hybrid tensor networks by considering trade-offs between the model complexity and practical performance. Firstly, we exploit a low-rank tensor-train deep neural network (TT-DNN) to build an end-to-end dee
Externí odkaz:
http://arxiv.org/abs/2203.06031
Autor:
Qi, Jun, Tejedor, Javier
This work aims to design a low complexity spoken command recognition (SCR) system by considering different trade-offs between the number of model parameters and classification accuracy. More specifically, we exploit a deep hybrid architecture of a te
Externí odkaz:
http://arxiv.org/abs/2201.10609
Autor:
Qi, Jun, Tejedor, Javier
This work investigates an extension of transfer learning applied in machine learning algorithms to the emerging hybrid end-to-end quantum neural network (QNN) for spoken command recognition (SCR). Our QNN-based SCR system is composed of classical and
Externí odkaz:
http://arxiv.org/abs/2110.08689
This paper proposes to generalize the variational recurrent neural network (RNN) with variational inference (VI)-based dropout regularization employed for the long short-term memory (LSTM) cells to more advanced RNN architectures like gated recurrent
Externí odkaz:
http://arxiv.org/abs/2009.01003
Submodular Rank Aggregation on Score-based Permutations for Distributed Automatic Speech Recognition
Publikováno v:
2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Distributed automatic speech recognition (ASR) requires to aggregate outputs of distributed deep neural network (DNN)-based models. This work studies the use of submodular functions to design a rank aggregation on score-based permutations, which can
Externí odkaz:
http://arxiv.org/abs/2001.10529
Autor:
Pereira, Daniel, Santamaria, Andreas, Pawar, Nisha, Carrascosa-Tejedor, Javier, Sardo, Mariana, Mafra, Luís, Guzmán, Eduardo, Owen, David J., Zaccai, Nathan R., Maestro, Armando, Marín-Montesinos, Ildefonso
Publikováno v:
In Colloids and Surfaces B: Biointerfaces July 2023 227
Autor:
Santamaria, Andreas, Carrascosa-Tejedor, Javier, Guzmán, Eduardo, Zaccai, Nathan R., Maestro, Armando
Publikováno v:
In Journal of Colloid And Interface Science January 2023 629 Part B:785-795
Unsupervised rank aggregation on score-based permutations, which is widely used in many applications, has not been deeply explored yet. This work studies the use of submodular optimization for rank aggregation on score-based permutations in an unsupe
Externí odkaz:
http://arxiv.org/abs/1707.01166