| Very deep convolutional neural networks for raw waveforms W Dai, C Dai, S Qu, J Li, S Das 2017 IEEE international conference on acoustics, speech and signal …, 2017 | 524 | 2017 |
| A comparison of deep learning methods for environmental sound detection J Li, W Dai, F Metze, S Qu, S Das 2017 IEEE International conference on acoustics, speech and signal …, 2017 | 174 | 2017 |
| Optimized adaptive scheduling of a manufacturing process system with multi-skill workforce and multiple machine types: An ontology-based, multi-agent reinforcement learning … S Qu, J Wang, S Govil, JO Leckie Procedia Cirp 57, 55-60, 2016 | 94 | 2016 |
| Adversarial music: Real world audio adversary against wake-word detection system J Li, S Qu, X Li, J Szurley, JZ Kolter, F Metze Advances in Neural Information Processing Systems 32, 2019 | 91 | 2019 |
| Online identification of inertia distribution in normal operating power system F Zeng, J Zhang, Y Zhou, S Qu IEEE Transactions on Power Systems 35 (4), 3301-3304, 2020 | 64 | 2020 |
| Large-scale traffic grid signal control with regional reinforcement learning T Chu, S Qu, J Wang 2016 american control conference (acc), 815-820, 2016 | 45 | 2016 |
| A centralized reinforcement learning approach for proactive scheduling in manufacturing S Qu, T Chu, J Wang, J Leckie, W Jian 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation …, 2015 | 45 | 2015 |
| Learning adaptive dispatching rules for a manufacturing process system by using reinforcement learning approach S Qu, J Wang, G Shivani 2016 IEEE 21st International Conference on Emerging Technologies and Factory …, 2016 | 42 | 2016 |
| Let blind people see: real-time visual recognition with results converted to 3D audio R Jiang, Q Lin, S Qu no. January, 2016 | 34 | 2016 |
| Comuptional reasoning and learning for smart manufacturing under realistic conditions S Qu, R Jian, T Chu, J Wang, T Tan 2014 International Conference on Behavioral, Economic, and Socio-Cultural …, 2014 | 23 | 2014 |
| Understanding audio pattern using convolutional neural network from raw waveforms S Qu, J Li, W Dai, S Das arXiv preprint arXiv:1611.09524, 2016 | 22 | 2016 |
| A comparison of deep learning methods for environmental sound J Li, W Dai, F Metze, S Qu, S Das arXiv preprint arXiv:1703.06902, 2017 | 18 | 2017 |
| Dynamic scheduling in large-scale stochastic processing networks for demand-driven manufacturing using distributed reinforcement learning S Qu, J Wang, J Jasperneite 2018 IEEE 23rd International Conference on Emerging Technologies and Factory …, 2018 | 17 | 2018 |
| Real-time decision support with reinforcement learning for dynamic flowshop scheduling J Wang, S Qu, J Wang, JO Leckie, R Xu Smart SysTech 2017; European Conference on Smart Objects, Systems and …, 2017 | 16 | 2017 |
| Acoustic scene recognition with deep neural networks (DCASE challenge 2016) JL Dai Wei, P Pham, S Das, S Qu Robert Bosch Research and Technology Center 3, 2016 | 16 | 2016 |
| Dynamic scheduling in modern processing systems using expert-guided distributed reinforcement learning S Qu, J Wang, J Jasperneite 2019 24th IEEE International Conference on Emerging Technologies and Factory …, 2019 | 15 | 2019 |
| Large-scale multi-agent reinforcement learning using image-based state representation T Chu, S Qu, J Wang 2016 IEEE 55th Conference on Decision and Control (CDC), 7592-7597, 2016 | 15 | 2016 |
| AudioTagging Done Right: 2nd comparison of deep learning methods for environmental sound classification JB Li, S Qu, PY Huang, F Metze arXiv preprint arXiv:2203.13448, 2022 | 13 | 2022 |
| Sound event detection for real life audio DCASE challenge JL Dai Wei, P Pham, S Das, S Qu, F Metze Proc. Workshop Detection and Classification of Acoustic Scenes and Events, 2016 | 12 | 2016 |
| 72‐3: Deep Learning Based Visual Defect Detection in Noisy and Imbalanced Data Q Cheng, S Qu, J Lee SID Symposium Digest of Technical Papers 53 (1), 971-974, 2022 | 11 | 2022 |
