303 citations · 381 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
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…
Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network Based Vector-to-Vector Regression
Jun Qi, Jun Du, Sabato Marco Siniscalchi +2
In this paper, we show that, in vector-to-vector regression utilizing deep neural networks (DNNs), a generalized loss of mean absolute error (MAE) between the predicted and expecte…
Variational Quantum Circuits for Deep Reinforcement Learning
Samuel Yen-Chi Chen, Chao-Han Huck Yang, Jun Qi +3
The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains. With…
Submodular Mini-Batch Training in Generative Moment Matching Networks
Jun Qi
This article was withdrawn because (1) it was uploaded without the co-authors' knowledge or consent, and (2) there are allegations of plagiarism.
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…