| A Dynamically Stabilized Recurrent Neural Network S Saab, Y Fu, A Ray, M Hauser Neural Processing Letters, 1-15, 2021 | 29 | 2021 |
| Neural network-based learning from demonstration of an autonomous ground robot Y Fu, DK Jha, Z Zhang, Z Yuan, A Ray Machines 7 (2), 24, 2019 | 21 | 2019 |
| Neural probabilistic forecasting of symbolic sequences with long short-term memory M Hauser, Y Fu, S Phoha, A Ray Journal of Dynamic Systems, Measurement, and Control 140 (8), 084502, 2018 | 14 | 2018 |
| Mad: Self-supervised masked anomaly detection task for multivariate time series Y Fu, F Xue 2022 International Joint Conference on Neural Networks (IJCNN), 1-8, 2022 | 11 | 2022 |
| ROPNN: Detection of ROP payloads using deep neural networks X Li, Z Hu, Y Fu, P Chen, M Zhu, P Liu arXiv preprint arXiv:1807.11110, 2018 | 10 | 2018 |
| A dynamically controlled recurrent neural network for modeling dynamical systems Y Fu, S Saab Jr, A Ray, M Hauser arXiv preprint arXiv:1911.00089, 2019 | 6 | 2019 |
| Bayesian nonparametric modeling of categorical data for information fusion and causal inference S Xiong, Y Fu, A Ray Entropy 20 (6), 396, 2018 | 5 | 2018 |
| Spatiotemporal representation learning with gan trained lstm-lstm networks Y Fu, S Sen, J Reimann, C Theurer 2020 IEEE International Conference on Robotics and Automation (ICRA), 10548 …, 2020 | 4 | 2020 |
| DeepReturn: A deep neural network can learn how to detect previously-unseen ROP payloads without using any heuristics X Li, Z Hu, H Wang, Y Fu, P Chen, M Zhu, P Liu Journal of Computer Security 28 (5), 499-523, 2020 | 4 | 2020 |
| Bayesian nonparametric regression modeling of panel data for sequential classification S Xiong, Y Fu, A Ray IEEE Transactions on Neural Networks and Learning Systems 29 (9), 4128-4139, 2017 | 4 | 2017 |
| Probabilistic forecasting of symbol sequences with deep neural networks M Hauser, Y Fu, Y Li, S Phoha, A Ray 2017 American Control Conference (ACC), 3147-3152, 2017 | 4 | 2017 |
| Masked multi-step multivariate time series forecasting with future information Y Fu, H Wang, N Virani arXiv preprint arXiv:2209.14413, 2022 | 2 | 2022 |
| Backpropagation through time and space: learning numerical methods with multi-agent reinforcement learning E Way, DSK Kapilavai, Y Fu, L Yu arXiv preprint arXiv:2203.08937, 2022 | 2 | 2022 |
| Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand Y Fu, N Virani, H Wang 2023 IEEE Power & Energy Society General Meeting (PESGM), 1-5, 2023 | 1 | 2023 |
| Multi-agent Learning of Numerical Methods for Hyperbolic PDEs with Factored Dec-MDP Y Fu, DSK Kapilavai, E Way International Conference on Practical Applications of Agents and Multi-Agent …, 2022 | 1 | 2022 |
| One Masked Model is All You Need for Sensor Fault Detection, Isolation and Accommodation Y Fu, W Yan arXiv preprint arXiv:2403.16153, 2024 | | 2024 |
| Probabilistic and semantic descriptions of image manifolds and their applications P Tu, Z Yang, R Hartley, Z Xu, J Zhang, Y Fu, D Campbell, J Singh, ... Frontiers in Computer Science 5, 1253682, 2023 | | 2023 |
| Deep Analysis Net with Causal Embedding for Coal-fired Power Plant Fault Detection and Diagnosis (DANCE4CFDD) F Xue, H Huang, Y Fu, B Feng, W Yan, T Wang GE Global Research, Niskayuna, New York (United States), 2021 | | 2021 |
| Trajectory Planning for Aerospace Vehicles using Deep Reinforcement Learning. D Kapilavai, S Roychowdhury, Y Fu, L Yu, BG van Bloemen Waanders Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021 | | 2021 |
