12 citations · 22 across the 7 of their papers we have counts for
7 papers
Optimizing Quantum Federated Learning Based on Federated Quantum Natural Gradient Descent
Jun Qi, Xiao-Lei Zhang, Javier Tejedor
Quantum federated learning (QFL) is a quantum extension of the classical federated learning model across multiple local quantum devices. An efficient optimization algorithm is alwa…
Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing
Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen +1
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-ran…
Exploiting Hybrid Models of Tensor-Train Networks for Spoken Command Recognition
Jun Qi, Javier Tejedor
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 accu…
Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks
Jun Qi, Javier Tejedor
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 re…
Variational Inference-Based Dropout in Recurrent Neural Networks for Slot Filling in Spoken Language Understanding
Jun Qi, Xu Liu, Javier Tejedor
This paper proposes to generalize the variational recurrent neural network (RNN) with variational inference (VI)-based dropout regularization employed for the long short-term memor…
Submodular Rank Aggregation on Score-based Permutations for Distributed Automatic Speech Recognition
Jun Qi, Chao-Han Huck Yang, Javier Tejedor
Distributed automatic speech recognition (ASR) requires to aggregate outputs of distributed deep neural network (DNN)-based models. This work studies the use of submodular function…
Unsupervised Submodular Rank Aggregation on Score-based Permutations
Jun Qi, Xu Liu, Javier Tejedor +1
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 opti…