| Variational quantum circuits for deep reinforcement learning SYC Chen, CHH Yang, J Qi, PY Chen, X Ma, HS Goan IEEE access 8, 141007-141024, 2020 | 364 | 2020 |
| On mean absolute error for deep neural network based vector-to-vector regression J Qi, J Du, SM Siniscalchi, X Ma, CH Lee IEEE Signal Processing Letters 27, 1485-1489, 2020 | 322 | 2020 |
| Decentralizing feature extraction with quantum convolutional neural network for automatic speech recognition CHH Yang, J Qi, SYC Chen, PY Chen, SM Siniscalchi, X Ma, CH Lee IEEE International Conference on Acoustics, Speech and Signal Processing …, 2021 | 168 | 2021 |
| Auditory Features based on Gammatone Filters for Robust Speech Recognition J Qi, D Wang, Y Jiang, R Liu IEEE International Symposium on Circuits and Systems (ISCAS), 2013 | 95 | 2013 |
| Characterizing speech adversarial examples using self-attention u-net enhancement CH Yang, J Qi, PY Chen, X Ma, CH Lee IEEE International Conference on Acoustics, Speech and Signal Processing …, 2020 | 64 | 2020 |
| Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network Based Vector-to-Vector Regression J Qi, J Du, SM Siniscalchi, X Ma, CH Lee IEEE Transactions on Signal Processing, 2020 | 58 | 2020 |
| When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing CHH Yang, J Qi, SYC Chen, Y Tsao, PY Chen IEEE International Conference on Acoustics, Speech and Signal Processing …, 2022 | 45 | 2022 |
| Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks J Qi, J Tejedor IEEE International Conference on Acoustics, Speech and Signal Processing …, 2022 | 45 | 2022 |
| Qtn-vqc: An end-to-end learning framework for quantum neural networks J Qi, CH Yang, PY Chen Physica Scripta 99 (1), 015111, 2023 | 41 | 2023 |
| Enhanced Adversarial Strategically-Timed Attacks Against Deep Reinforcement Learning CHH Yang, J Qi, PY Chen, Y Ouyang, ITD Hung, CH Lee, X Ma IEEE International Conference on Acoustics, Speech and Signal Processing …, 2020 | 40 | 2020 |
| A theory on deep neural network based vector-to-vector regression with an illustration of its expressive power in speech enhancement J Qi, J Du, SM Siniscalchi, CH Lee IEEE/ACM Transactions on Audio, Speech, and Language Processing 27 (12 …, 2019 | 39 | 2019 |
| Theoretical error performance analysis for variational quantum circuit based functional regression J Qi, CHH Yang, PY Chen, MH Hsieh npj Quantum Information 9 (1), 4, 2023 | 35 | 2023 |
| Bottleneck Features based on Gamma-tone Frequency Cepstral Coefficients J Qi, D Wang, J Xu, J Tejedor Interspeech, 2013 | 33 | 2013 |
| Designing tensor-train deep neural networks for time-varying MIMO channel estimation J Zhang, X Ma, J Qi, S Jin IEEE Journal of Selected Topics in Signal Processing 15 (3), 759-773, 2021 | 28 | 2021 |
| Deep multi-view representation learning for multi-modal features of the schizophrenia and schizo-affective disorder J Qi, J Tejedor 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016 | 23 | 2016 |
| Tensor-to-vector regression for multi-channel speech enhancement based on tensor-train network J Qi, H Hu, Y Wang, CHH Yang, SM Siniscalchi, CH Lee IEEE International Conference on Acoustics, Speech and Signal Processing …, 2020 | 22* | 2020 |
| Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing J Qi, CHH Yang, PY Chen, J Tejedor IEEE/ACM Transactions on Audio, Speech, and Language Processing 31, 633-642, 2023 | 20 | 2023 |
| Federated quantum natural gradient descent for quantum federated learning J Qi, MH Hsieh Federated Learning, 329-341, 2024 | 14 | 2024 |
| Subspace Models for Bottleneck Features J Qi, D Wang, J Tejedor Interspeech, 2013 | 14 | 2013 |
| Exploring Deep Hybrid Tensor-to-Vector Network Architectures for Regression Based Speech Enhancement J Qi, H Hu, Y Wang, CHH Yang, SM Siniscalchi, CH Lee Interspeech, 2020 | 13 | 2020 |
Chao-Han Huck YangSr. Research Scientist, NVIDIA ResearchVerified email at nvidia.com
