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ORCIDJun Qi 0002
Person information
- affiliation (PhD 2022): Georgia Institute of Technology, School of Electrical and Computer Engineering, Atlanta, USA
- affiliation: Researcher at Microsoft Research, Deep Learning Technology Center
- affiliation (former): Tsinghua University, Department of Electronic Engineering, China
- affiliation (former): University of Washington, Seattle, WA, USA
Other persons with the same name
- Jun Qi — disambiguation page
- Jun Qi 0001
![[0000-0002-8761-8318]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-8761-8318")
— Xi'an Jiaotong-Liverpool University, Department of Computer Science and Software Engineering, Suzhou, China (and 4 more) - Jun Qi 0003 — Chengdu University of Information Technology, School of Control Engineering, China (and 3 more)
- Jun Qi 0004 — Qinghai University, Department of Computer Technology and Application, China
- Jun Qi 0005 — Chongqing University Cancer Hospital, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, China
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2020 – today
- 2023
[j7]Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen, Javier Tejedor:
Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing. IEEE ACM Trans. Audio Speech Lang. Process. 31: 633-642 (2023)- [j6]Muhammad Shahmeer Omar, Jun Qi, Xiaoli Ma:
Mitigating Clipping Distortion in Multicarrier Transmissions Using Tensor-Train Deep Neural Networks. IEEE Trans. Wirel. Commun. 22(3): 2127-2138 (2023) - [c19]Jun Qi, Xiao-Lei Zhang, Javier Tejedor:
Optimizing Quantum Federated Learning Based on Federated Quantum Natural Gradient Descent. ICASSP 2023: 1-5 - [i19]Jun Qi, Xiao-Lei Zhang, Javier Tejedor:
Optimizing Quantum Federated Learning Based on Federated Quantum Natural Gradient Descent. CoRR abs/2303.08116 (2023) - [i18]Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen, Min-Hsiu Hsieh:
Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits. CoRR abs/2306.03741 (2023) - 2022
- [b1]Jun Qi:
Theoretical Error Performance Analysis for Deep Neural Network Based Regression Functional Approximation. Georgia Institute of Technology, Atlanta, GA, USA, 2022 - [c18]Jun Qi, Javier Tejedor:
Exploiting Hybrid Models of Tensor-Train Networks For Spoken Command Recognition. ICASSP 2022: 3114-3118 - [c17]Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen, Yu Tsao, Pin-Yu Chen:
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing. ICASSP 2022: 8602-8606 - [c16]Jun Qi, Javier Tejedor:
Classical-To-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks. ICASSP 2022: 8627-8631 - [c15]Chao-Han Huck Yang, Jun Qi, Sabato Marco Siniscalchi, Chin-Hui Lee:
An Ensemble Teacher-Student Learning Approach with Poisson Sub-sampling to Differential Privacy Preserving Speech Recognition. ISCSLP 2022: 1-5 - [i17]Jun Qi, Javier Tejedor:
Exploiting Hybrid Models of Tensor-Train Networks for Spoken Command Recognition. CoRR abs/2201.10609 (2022) - [i16]Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen, Yu Tsao, Pin-Yu Chen:
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing. CoRR abs/2203.03550 (2022) - [i15]Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen, Javier Tejedor:
Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing. CoRR abs/2203.06031 (2022) - [i14]Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen, Min-Hsiu Hsieh:
Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regression. CoRR abs/2206.04804 (2022) - [i13]Chao-Han Huck Yang, Jun Qi, Sabato Marco Siniscalchi, Chin-Hui Lee:
An Ensemble Teacher-Student Learning Approach with Poisson Sub-sampling to Differential Privacy Preserving Speech Recognition. CoRR abs/2210.06382 (2022) - 2021
- [j5]Jing Zhang, Xiaoli Ma, Jun Qi, Shi Jin:
Designing Tensor-Train Deep Neural Networks For Time-Varying MIMO Channel Estimation. IEEE J. Sel. Top. Signal Process. 15(3): 759-773 (2021) - [c14]Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen, Pin-Yu Chen, Sabato Marco Siniscalchi, Xiaoli Ma, Chin-Hui Lee:
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition. ICASSP 2021: 6523-6527 - [i12]Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen:
QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks. CoRR abs/2110.03861 (2021) - [i11]Jun Qi, Javier Tejedor:
Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks. CoRR abs/2110.08689 (2021) - 2020
- [j4]Samuel Yen-Chi Chen, Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Xiaoli Ma, Hsi-Sheng Goan:
Variational Quantum Circuits for Deep Reinforcement Learning. IEEE Access 8: 141007-141024 (2020) - [j3]Jun Qi, Jun Du, Sabato Marco Siniscalchi, Xiaoli Ma, Chin-Hui Lee:
On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression. IEEE Signal Process. Lett. 27: 1485-1489 (2020) - [j2]Jun Qi, Jun Du, Sabato Marco Siniscalchi, Xiaoli Ma, Chin-Hui Lee:
Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network-Based Vector-to-Vector Regression. IEEE Trans. Signal Process. 68: 3411-3422 (2020) - [c13]Jun Qi, Xiaoli Ma, Chin-Hui Lee, Jun Du, Sabato Marco Siniscalchi:
Performance Analysis for Tensor-Train Decomposition to Deep Neural Network Based Vector-to-Vector Regression. CISS 2020: 1-6 - [c12]Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Xiaoli Ma, Chin-Hui Lee:
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement. ICASSP 2020: 3107-3111 - [c11]Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Yi Ouyang, I-Te Danny Hung, Chin-Hui Lee, Xiaoli Ma:
Enhanced Adversarial Strategically-Timed Attacks Against Deep Reinforcement Learning. ICASSP 2020: 3407-3411 - [c10]Jun Qi, Chao-Han Huck Yang, Javier Tejedor:
Submodular Rank Aggregation on Score-Based Permutations for Distributed Automatic Speech Recognition. ICASSP 2020: 3517-3521 - [c9]Jun Qi, Hu Hu, Yannan Wang, Chao-Han Huck Yang, Sabato Marco Siniscalchi, Chin-Hui Lee:
Tensor-To-Vector Regression for Multi-Channel Speech Enhancement Based on Tensor-Train Network. ICASSP 2020: 7504-7508 - [c8]Jun Qi, Hu Hu, Yannan Wang, Chao-Han Huck Yang, Sabato Marco Siniscalchi, Chin-Hui Lee:
Exploring Deep Hybrid Tensor-to-Vector Network Architectures for Regression Based Speech Enhancement. INTERSPEECH 2020: 76-80 - [i10]Jun Qi, Chao-Han Huck Yang, Javier Tejedor:
Submodular Rank Aggregation on Score-based Permutations for Distributed Automatic Speech Recognition. CoRR abs/2001.10529 (2020) - [i9]Jun Qi, Hu Hu, Yannan Wang, Chao-Han Huck Yang, Sabato Marco Siniscalchi, Chin-Hui Lee:
Tensor-to-Vector Regression for Multi-channel Speech Enhancement based on Tensor-Train Network. CoRR abs/2002.00544 (2020) - [i8]Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Yi Ouyang, I-Te Danny Hung, Chin-Hui Lee, Xiaoli Ma:
Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning. CoRR abs/2002.09027 (2020) - [i7]Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Xiaoli Ma, Chin-Hui Lee:
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement. CoRR abs/2003.13917 (2020) - [i6]Jun Qi, Hu Hu, Yannan Wang, Chao-Han Huck Yang, Sabato Marco Siniscalchi, Chin-Hui Lee:
Exploring Deep Hybrid Tensor-to-Vector Network Architectures for Regression Based Speech Enhancement. CoRR abs/2007.13024 (2020) - [i5]Jun Qi, Jun Du, Sabato Marco Siniscalchi, Xiaoli Ma, Chin-Hui Lee:
Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network Based Vector-to-Vector Regression. CoRR abs/2008.05459 (2020) - [i4]Jun Qi, Jun Du, Sabato Marco Siniscalchi, Xiaoli Ma, Chin-Hui Lee:
On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression. CoRR abs/2008.07281 (2020) - [i3]Jun Qi, Xu Liu, Javier Tejedor:
Variational Inference-Based Dropout in Recurrent Neural Networks for Slot Filling in Spoken Language Understanding. CoRR abs/2009.01003 (2020) - [i2]Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen, Pin-Yu Chen, Sabato Marco Siniscalchi, Xiaoli Ma, Chin-Hui Lee:
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition. CoRR abs/2010.13309 (2020)
2010 – 2019
- 2019
- [j1]Jun Qi, Jun Du, Sabato Marco Siniscalchi, Chin-Hui Lee:
A Theory on Deep Neural Network Based Vector-to-Vector Regression With an Illustration of Its Expressive Power in Speech Enhancement. IEEE ACM Trans. Audio Speech Lang. Process. 27(12): 1932-1943 (2019) - 2018
- [c7]Jun Qi, Xu Liu, Shunsuke Kamijo, Javier Tejedor:
Distributed Submodular Maximization for Large Vocabulary Continuous Speech Recognition. ICASSP 2018: 2501-2505 - 2017
- [i1]Jun Qi, Javier Tejedor:
Unsupervised Submodular Rank Aggregation on Score-based Permutations. CoRR abs/1707.01166 (2017) - 2016
- [c6]Jun Qi, Javier Tejedor:
Deep multi-view representation learning for multi-modal features of the schizophrenia and schizo-affective disorder. ICASSP 2016: 952-956 - [c5]Jun Qi, Javier Tejedor:
Robust submodular data partitioning for distributed speech recognition. ICASSP 2016: 2254-2258 - 2015
- [c4]Chao Ma, Jun Qi, Dongmei Li, Runsheng Liu:
Improving bottleneck features for automatic speech recognition using gammatone-based cochleagram and sparsity regularization. APSIPA 2015: 63-67 - 2013
- [c3]Jun Qi, Dong Wang, Javier Tejedor:
Subspace models for bottleneck features. INTERSPEECH 2013: 1746-1750 - [c2]Jun Qi, Dong Wang, Ji Xu, Javier Tejedor:
Bottleneck features based on gammatone frequency cepstral coefficients. INTERSPEECH 2013: 1751-1755 - [c1]Jun Qi, Dong Wang, Yi Jiang, Runsheng Liu:
Auditory features based on Gammatone filters for robust speech recognition. ISCAS 2013: 305-308
Coauthor Index
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